Data Engineering Courses

Are you interested in a career that combines cutting-edge technology with high demand and lucrative salaries? Maybe you should be taking a closer look at data engineering jobs! In this guide, you'll: Discover 8 of the hottest data engineering jobs Learn the key skills required for each position Get insights on typical responsibilities and salary ranges As companies increasingly rely on data to drive business decisions, skilled data engineers are in high demand across all industries. As The Times puts it, crunch the numbers and it pays to work in data. With the right expertise, you can secure a rewarding career in data engineering. Read on to explore the exciting opportunities awaiting you in this fast-growing field. Why Choose a Career in Data Engineering? A career as a Data Engineer offers a unique blend of technical challenges, growth opportunities, and tangible impact on business success. The numbers speak for themselves: The global big data market is projected to hit $862.31 billion by 2030. This represents an impressive compound annual growth rate (CAGR) of 14.9%. There are many reasons to consider a career in data engineering. Here are some of the key benefits that make this field so compelling: High Job security: With the data industry expanding rapidly, demand for data engineers is consistently strong. You can feel confident in the stability and longevity of this career path. Ample Growth Opportunities: Data engineering jobs are constantly evolving. You'll have the chance to work on cutting-edge projects involving cloud technologies, machine learning, and more. There's no shortage of opportunities to learn and advance. Significant Impact: As a data engineer, you'll be critical in shaping business strategies. By building robust data management and analytics infrastructures, you'll enable organizations to make data-driven decisions that drive success. On top of all this, data engineering roles often come with competitive salaries and comprehensive benefits packages. This reflects the indispensable value that data engineers bring to their organizations. So, which specific data engineering job might suit you? In the next section, we'll explore eight high-demand roles to help you identify your ideal fit. Top 8 Data Engineering Jobs If you're looking to break into the booming data engineering field, here are the top 8 roles to consider, based on current industry trends and demands: Data Engineer Big Data Engineer Machine Learning Engineer Data Architect Cloud Data Engineer ETL Developer Data Operations Engineer (DataOps) AI Data Engineer In the sections below, we'll explore each of these data engineering jobs in greater detail, covering key responsibilities, must-have skills, and tips for success. 1. Data Engineer Data engineers are the backbone of an organization's data operations. They design and maintain the systems that allow companies to collect, process, and analyze huge amounts of information. If you become a data engineer, you'll play an important role in helping businesses make data-driven decisions. Unlike other data roles that might involve interpreting data or building complex models, data engineers focus mainly on the technical infrastructure needed to handle data at scale. This includes things like data pipelines, databases, and computing systems. You aim to ensure data is reliable, efficient, and accessible to everyone who needs it. On a typical day, a data engineer might integrate new data sources, run ETL (extract, transform, load) processes to prep data for analysis, and work with teams across the company to support their data needs. You'll design databases, optimize queries, monitor system performance, and implement security measures to keep data safe. Salary: $111K – $164K/yr (Glassdoor) Responsibilities: Design and implement efficient, reliable data storage and processing systems Build and maintain data pipelines that feed analytics and ML models Optimize data infrastructure for performance and scalability Collaborate with data consumers to understand and meet data needs Key Skills: Strong programming skills (Java/Scala/Python/R) Expert knowledge of SQL and NoSQL databases Experience with big data tools like Hadoop, Spark, Kafka Understanding of data structures, algorithms, and distributed systems Great problem-solving and communication abilities Data engineers are the foundation of any data-driven organization. They build and maintain the critical data infrastructure that enables analysts, scientists, and leaders to derive insights and make informed decisions. 2. Big Data Engineer As data becomes the foundation for more and more decision-making, organizations are collecting vast amounts of information - often in the petabyte range. To effectively process and utilize these massive datasets, they turn to the expertise of Big Data Engineers. These professionals specialize in designing and managing large-scale data systems, ensuring businesses can quickly and efficiently transform raw data into valuable insights. What sets Big Data Engineers apart from typical Data Engineers is their ability to handle enormous volumes of data using advanced technologies like Hadoop and Spark. On a day-to-day basis, Big Data Engineers might be found architecting scalable data infrastructures, developing robust ETL processes to integrate new data sources, or fine-tuning system performance to keep up with the demands of big data. By enabling companies to harness the power of their data, these professionals play a crucial role in driving data-informed decision-making and maintaining a competitive edge. Salary: $105K – $157K/yr (Glassdoor) Responsibilities: Designing and maintaining scalable big data architectures Developing robust ETL processes and pipelines Ensuring high-quality data integration and accessibility Optimizing system performance for large-scale data operations Collaborating with teams to align big data strategies with business goals Key Skills: Expertise in distributed computing frameworks (e.g., Hadoop, Spark) Proficiency in data modeling and database management (SQL/NoSQL) Strong programming abilities in languages like Java or Python Familiarity with data warehousing solutions Sharp problem-solving skills and a knack for critical thinking As companies continue to amass more data, the demand for skilled Big Data Engineers shows no signs of slowing down. For those with the right mix of technical prowess and business acumen, a career in this field offers the opportunity to be at the forefront of the big data revolution. 3. Machine Learning Engineer Machine Learning Engineers bridge the gap between data science theory and real-world applications. They specialize in deploying and managing machine learning models in production environments. While Data Engineers build the infrastructure for processing and storing data, Machine Learning Engineers ensure ML models are smoothly integrated into larger systems. On a typical day, a Machine Learning Engineer might design ML systems, research new algorithms, or develop ML applications to solve practical problems. They are crucial in turning data science models into scalable, dependable solutions directly impacting how an organization operates and makes decisions. Salary: $133K – $214K/yr (Glassdoor) Responsibilities Selecting the best ML algorithms for various use cases Conducting experiments to improve ML model accuracy and efficiency Making ML pipelines scalable for big data and high traffic Integrating ML models with other parts of the production system Monitoring and tuning existing ML systems for best performance Key Skills Solid coding abilities in Python, R, SQL, and related languages In-depth understanding of ML algorithms and data structures Familiarity with distributed computing tools like Hadoop or Spark Skill in explaining technical concepts to non-technical people Strong critical thinking and problem-solving under pressure For organizations wanting to leverage ML and AI in their products, services, and internal workflows, Machine Learning Engineers are indispensable. As the use of machine learning grows across industries, so will the need for capable Machine Learning Engineers. 4. Data Architect While Data Engineers focus on the technical details, Data Architects take a big-picture view, creating the blueprint that guides a company's overall data strategy and infrastructure. They design the frameworks for effectively managing an organization's data assets and optimize systems to improve data quality and access. On a typical day, a Data Architect might use data modeling tools to map out data structures. They work with various teams to ensure data initiatives support business goals. Salary: $153K – $235K/yr (Glassdoor) Responsibilities Develop comprehensive data strategies aligned with organizational objectives Design and maintain scalable data architectures Implement effective governance of metadata, master data, and reference data Ensure database systems are efficient, reliable, and secure Key Skills Expertise in data modeling and design Proficiency with SQL and database administration Knowledge of system development lifecycles and project management Ability to lead enterprise-scale data platforms like data warehouses In short, Data Architects play a vital role in helping organizations harness the strategic power of data. By creating the frameworks to manage data as a critical asset, they enable smarter, data-driven decisions across the enterprise. 5. Cloud Data Engineer A Cloud Data Engineer designs, builds, and runs data systems on cloud platforms like AWS and Azure. They're experts at maximizing the cloud's flexibility, speed, and efficiency to handle huge amounts of data. What sets Cloud Data Engineers apart is their laser focus on cloud solutions. While Data Engineers work on data pipelines in general, Cloud Data Engineers zero in on doing it all in the cloud. They're the cloud specialists of the data world. On a typical day, you'll find a Cloud Data Engineer setting up and managing databases in the cloud, automating data workflows, and using DevOps techniques to keep things running smoothly. The goal? To make sure the company's data is reliable, accessible, and driving the business forward. Salary: $107K – $164K/yr (Glassdoor) Responsibilities Design and build scalable cloud databases Create automated data pipelines for real-time processing Ensure high data quality and accessibility for analytics Maintain tight data security in the cloud Key Skills Expertise in AWS, Google Cloud, or Azure Know-how with big data tools like Hadoop and Spark Experience with automation and infrastructure as code Strong data modeling skills for top-notch database design Cloud Data Engineers play an integral role in using the power of the cloud to store, process, and analyze massive datasets. Their work is key to helping companies stay agile, informed, and competitive. 6. ETL Developer ETL Developers specialize in the crucial Extract, Transform, and Load (ETL) process that turns raw data into analysis-ready insights. While Data Engineers handle the full pipeline, ETL Developers focus specifically on preparing data for business intelligence. What sets these professionals apart is their expertise in data transformation. They spend their days extracting data from diverse sources, reshaping it to align with business needs, and loading it into warehouses. This process enables companies to uncover strategic insights and maintain a competitive edge. On a typical day, an ETL Developer might collaborate with analysts to clarify requirements, optimize pipelines for peak performance, and troubleshoot any issues. It's a role demanding equal parts technical skill and problem-solving finesse. Salary: $113K – $165K/yr (Glassdoor) Responsibilities Manage and fine-tune data pipelines for optimal efficiency and precision Transform data to meet business specifications while ensuring high quality Partner with analysts and business teams to gather requirements Safeguard data integrity throughout the ETL process Key Skills Deep SQL and database management expertise Hands-on experience with ETL tools like Talend or Informatica Solid grasp of data warehousing architecture and solutions Sharp troubleshooting skills to navigate pipeline challenges ETL Developers are critical for transforming raw data. Their work forms the very foundation of informed decision-making. 7. Data Operations Engineer (DataOps) DataOps Engineers play a valuable role in optimizing how organizations leverage data. By integrating data engineering, data science, and DevOps practices, they enhance the flow of data from source to end users. This enables companies to make data-driven decisions faster and maintain a competitive edge. What sets DataOps Engineers apart is their focus on automation and efficiency. They continuously refine data pipelines and processes to accelerate development cycles and improve data quality. Collaborating closely with technical teams and stakeholders, they ensure the data ecosystem effectively supports business objectives. On a typical day, a DataOps Engineer manages and optimizes data pipelines. They automate processes, implement scalable storage solutions, and align data strategies with organizational goals. Salary: $99K – $150K/yr (Glassdoor) Responsibilities Design, develop, and maintain scalable data pipelines Automate processes to enhance pipeline efficiency and reliability Implement data storage solutions that support scalability and performance Collaborate with cross-functional teams to align data strategies with business objectives Key Skills Proficiency in programming languages such as Python or Java In-depth knowledge of ETL tools and practices Strong problem-solving and critical thinking abilities Familiarity with cloud services, containerization, and orchestration technologies By optimizing the data lifecycle from end to end, DataOps Engineers help organizations become more agile, insightful, and innovative. Companies that invest in strong DataOps practices can make smarter decisions faster, giving them a powerful competitive advantage. 8. AI Data Engineer While traditional Data Engineers focus broadly on data pipelines and storage, AI Data Engineers specialize in building the infrastructure needed to deploy and scale machine learning models. As more businesses leverage machine learning for decision-making, this role has become increasingly popular. On a typical day, an AI Data Engineer might develop data pipelines, ensure data security, and work with data scientists to improve machine learning operations. Through these efforts, they play an important part in implementing and maintaining the technology that powers advanced analytics. Salary: $160K – $266K/yr (Glassdoor) Responsibilities Build robust ML infrastructure to support the machine learning lifecycle Maintain high data quality standards for reliable model training and analysis Collaborate with technical teams to integrate AI systems into business processes Key Skills Deep knowledge of data architecture and database management systems Proficiency in Python, Java, and other languages for building data pipelines Experience with cloud services (AWS, Azure) and big data tools (Spark, Hadoop) Understanding of AI algorithms used to develop scalable machine learning models With the growing adoption of AI and machine learning across industries, the demand for skilled AI Data Engineers will likely continue to rise. These professionals bridge the gap between raw data and deployable models, making them essential to any data-driven organization. How to Prepare for Data Engineering Roles If you want to land a data engineering job, building the right skills is key. Focus on learning programming languages like Python and Java, SQL databases, and data warehousing solutions. Familiarity with big data tools like Hadoop and Apache Spark is also a must-have. However, theoretical knowledge is not enough. You need to apply your skills in the real world. Take on challenging projects like log analytics, where you collect and clean server data, store it using technologies like Apache NiFi and HDFS, then analyze it with Apache Spark to gain insights. Hands-on experience is invaluable for developing your abilities and impressing employers with a porfolio of projects that show off what you can do. As data-related roles continue to evolve and new hybrid positions like Data Engineering Analyst emerge, marrying traditional engineering with analysis, expanding your skill set is important. Dedicate yourself to ongoing education through courses, workshops, side projects, and experimentation. Some key skills to focus on: Programming languages (Python, Java) SQL databases Data warehousing solutions Big data tools (Hadoop, Apache Spark) Combining technical skills, practical experience, and continuous learning is key to success in data engineering jobs. Data engineering courses can help you start to build your skills. How to Determine If a Data Engineering Role Is the Right Fit While data engineers share some common foundations with other data roles like programming, databases, and data processing, they each have distinct focus areas. To determine which path might suit you best, consider your technical background, interests, and goals: Do you have experience with programming languages like Python, Java, or Scala? Are you interested in working with large-scale data storage and processing systems? Does building and deploying machine learning models excite you? Are you drawn to taking a strategic leadership role in data management? If you answered "yes" to most of those questions, there's a good chance you'd find a career in data engineering highly rewarding. Career Paths and Continuous Learning Data engineering offers diverse paths for growth and specialization. You might start as a generalist Data Engineer and progress into a niche like Machine Learning or Data Architecture. Whichever path you choose, a commitment to ongoing skill development is key. This field's tools and best practices evolve quickly, so continuous learning is a must. Next Steps Data engineering continues to play a vital role in driving technological progress and business efficiency. The field encompasses a range of roles critical for managing and making sense of the massive volumes of data generated by today's technologies. In this post, we explored several key data engineering jobs, each contributing to the rapidly evolving tech landscape in its own way. Whether your interests lie in creating scalable data infrastructures or developing complex data processing systems, there's a data engineering job out there that can put your skills to good use. Our mission at Dataquest is to equip you with the resources and guidance to launch a successful data engineering career and thrive in this challenging but deeply rewarding field. We invite you to explore our Data Engineer Career Path and other offerings to see how we can help you achieve your goals.

Exciting news: we have a Data Engineering path that offers from-scratch training for anyone who wants to become a data engineer or learn some data engineering skills. Looks cool, right? But it begs the question: Why Learn Data Engineering? Typically, data science teams are comprised of data analysts, data scientists, and data engineers. We’ve talked about the differences between these data roles, but here let’s go deeper into some of the advantages of being a data engineer. Data engineers are the people who connect all the pieces of the data ecosystem within a company or institution. They accomplish this by doing things like: Accessing, collecting, auditing, and cleaning data from applications and systems into a usable state Creating and maintaining efficient databases Building data pipelines Monitoring and managing all the data systems (scalability, security, etc) Implementing data scientists’ output in a scalable manner Data Engineer vs. Data Scientist Doing everything listed above primarily requires one particular skill: programming. Data engineers are software engineers who specialize in data and data technologies. That makes them quite different from data scientists, who certainly have programming skills, but who typically aren’t engineers. It’s not uncommon for data scientists to hand over their work (e.g., a recommendation system) to data engineers for actual implementation. And while it’s data analysts and data scientists who are doing the analysis, it’s typically data engineers who are building the data pipelines and other systems necessary to make sure that everyone has easy access to the data they need (and that no one has access to the data who shouldn’t). A strong foundation in software engineering and programming equips data engineers to build the tools data teams and their companies need to succeed. Or, as Jeff Magnusson put it: I like to think of it in terms of Lego blocks. Engineers design new Lego blocks that data scientists assemble in creative ways to create new data science. This brings us to the first reason why you might want to learn data engineering: 1. It’s the Backbone of Data Science Data engineers are on the front lines of data strategy so that others don’t need to be. They are the first people to tackle the influx of structured and unstructured data that enters a company’s systems. They are the foundation of any data strategy. Without Lego blocks, after all, you can’t build a Lego castle. In the above Data Science Hierarchy of Needs (proposed by Monica Rogati), data engineers are completely responsible for the two bottom rows, and share responsibility with data analysts and data scientists for the third row from the bottom. How Critical Is Data Engineering? To answer this, imagine the pyramid pictured above is used as a funnel and flipped upside down. Data is poured into the top of that funnel, and the first people to touch it are data engineers. The more efficient they are at filtering, cleaning, and directing that data, the more efficient everything else can be as the data flows further down the funnel and towards other team members. Conversely, if the data engineers are not efficient, they can serve as a block in the funnel that harms the work of everyone downstream. If, for example, a poorly-built data pipeline ends up feeding the data science team incomplete data, any analysis they perform on that data may be useless. In this way, data engineers act as multipliers of the outcomes of a data strategy. They are the giants on whose shoulders data analysts and data scientists stand. This is evidenced in the way companies with good data strategies structure their teams. According to Jesse Anderson, a data engineer and managing director of the Big Data Institute: A common starting point is 2-3 data engineers for every data scientist. For some organizations with more complex data engineering requirements, this can be 4-5 data engineers per data scientist. 2. It’s Technically Challenging One of the Python functions data analysts and scientists use the most is read_csv — from the pandas library. This function reads tabular data stored in a text file into Python, so that it can be explored and manipulated. If you’ve worked with data in Python before, you’re probably very used to typing something like this: import pandas as pd df = pd.read_csv("a_text_file.csv") Easy and convenient, right? The read_csv function is a great example of the essence of software engineering: creating abstract, broad, efficient, and scalable solutions. What does that mean and how does it relate to learning data engineering? Let’s take a deeper look. Abstract: When reading a file in a computer, a very complex process occurs under the hood. However, our use of the function is very simple because what goes on in the background is abstracted away from the usage. You don’t need to understand what read_csv is doing “under the hood” to use it effectively. Broad: This function also allows us to explicitly choose what delimiter is being used in the text’s file tabular data (e.g. commas, semicolons, tabs, and so on). This makes it easy to use with a variety of CSV styles, and that’s music to data scientists’ ears. And there are many other options that allow data practitioners to focus on their goals instead of having to worry about programming details. Efficient: read_csv works quickly and efficiently, and it’s also efficient to read in code. Scalable: Another option included with this function allows us to read files by chunks, so that if a file is too large to read into the computer’s RAM, it can be read chunk by chunk, allowing users to process files as large as they come. Robert A. Heinlein is famous for having said that: One man’s magic is another man’s engineering. It is data engineers who work that magic, building tools like the read_csv function that are abstract, broad, efficient, and scalable so that the rest of the team can focus on the data itself and their analysis rather than having to struggle with programming puzzles. At the same time, data engineering probably requires less math than data science, so if you prefer programming over mathematics, data engineering could be an ideal option for you! 3. It’s Rewarding Making data scientists’ lives easier isn’t the only thing that motivates data engineers. There’s no denying that data engineers are making a significant and growing impact on the world at large. According to recent statistics, we create more than 402 billion gigabytes of data every single day. If that wasn't enough, it's believed that 90% of the world's data was created in just the last two years. This immensity of data has made data engineers more valuable than ever. IoT Analytics estimated more than 18.8 billion IoT devices at the end of 2024, up from about 16.6 billion in 2023, and 14.4 billion in 2022. With all this growth comes a lot more data from a lot more sources, and thus a lot more need for data engineers who can effectively process and channel it. This means that data engineers have a huge variety of ways they can pursue their interests and deepen their skill sets. To give you an idea of how vast this world is, here’s a list of popular data tools and technologies:  Amazon Redshift Amazon S3 Apache Cassandra Apache HBase Apache Kafka Apache Spark Apache Zookeeper Azure ElephantDB Hadoop Distributed File System IBM DB2 MapReduce Memcached Microsoft SQL Server Mongo DB Oracle Database PostgreSQL Redis SQLite Storm SAP IQ Teradata Vertica Of course, a data engineer doesn’t have to know all of these, but this list illustrates just how much there is to do in the world of data engineering. Once you’ve got the skills to get jobs, you’ve got a lot of freedom to choose what you’re working on and what tools you’re working with. Since data engineers have both data and software engineering skills, they’re also capable of building a variety of products. Want to contribute to an early-stage startup, or become an entrepreneur and found your own someday? Data engineering skills give you the tools you’ll need to both build great products and analyze how those products are performing. You’ll be able to implement and measure the success of pretty much anything you can think of. Want to work remotely? According to The U.S. Bureau of Labor and Statistics: The share of employed people who spent time working at home on days they worked was about the same as in 2022 (34 percent) but higher than in 2019 (24 percent), before the COVID-19 pandemic. So if working outside of the office is something that suits you, data engineering could help you achieve that goal. Because there’s a high demand for data engineers, and because most of the work can be done remotely, it’s definitely possible to find remote data engineering jobs, or work for yourself as a freelance contractor on shorter-term data engineering projects. Finally, data engineers also have plenty of opportunities to give back to the community. According to 2024’s Stack Overflow Developer’s Survey: 93% of respondents visit Stack Overflow at least multiple times per month if not multiple times per day. And since you’ll have data and engineering skills, you’ll be able to make a real difference developing cool new tools for the data science community. 4. It Pays Well You should never take a job based only on the salary, but there’s no denying that salary is important! According to Glassdoor, a machine learning engineer gets paid an average of $168,000. Advertised data scientist positions pay an average of $164,000. And data engineering positions pay an average of $202,000.” It’s no surprise as to why. Data engineering skills like Python, SQL, and the shell regularly rank among the highest-paying skills in StackOverflow’s developer surveys. And at the time of this writing, there are around 140,000 results for the search term Data Scientist on LinkedIn, and around 251,000 results for the search term Data Engineer. A majority of companies report difficulty finding qualified data engineers. In a recent survey, 87% of tech leaders said they struggle to secure skilled tech talent in the current market. This persistent talent shortage shows that even though more people are entering the field, demand is still outpacing supply. Looking ahead, the job market for data engineers appears resilient. The tech industry’s recent “reset” (with some layoffs in 2022–23) has largely been a correction of over-hiring, and experts anticipate data roles will remain in demand through 2025. And that’s not all! According to Statista: The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027 5. It’s Valuable Even If You Don’t Want to Be a Data Engineer Even if you don’t want to pursue a career as a data engineer, if you want to work in data science, it can be very useful to have some knowledge of data engineering. The benefits are multifold: As a data practitioner, there’s a good chance you’ll periodically be asked to complete tasks that have some overlap with other job roles, including data engineering. Learning a different way of looking at things can be beneficial to your understanding, and it gives you an opportunity to brush up on skills you might not have used in a while. Having engineering skills will make you more self-sufficient. This can help your career tremendously as you need not be blocked anymore, waiting for someone to do something for you. Learning data engineering skills will allow you to empathize with data engineers and better communicate with them. This will also help your team, as you can become the bridge that connects yours to the data engineering team. Wrapping Up and Next Steps We’ve looked at five good reasons why data engineering is such an exciting and rewarding field: it’s the backbone of data science, it’s technically challenging, it’s deeply impactful, it pays well, and it’s valuable even if you never formally become a data engineer. When it comes right down to it, data engineering is all about designing systems that make data accessible, reliable, and ready for analysis. It’s where the Legos that build our data-fueled future are snapped together to create new data science. If you’re ready to take the next step toward becoming a data engineer—or just want to add some invaluable engineering skills to your data toolkit—consider enrolling in our Data Engineering career path. We’ve designed it from scratch to give you the hands-on experience and foundational knowledge you need. Best of luck to you and happy coding!

From helping cars drive themselves to helping Facebook tag you in photos, data science has attracted a lot of buzz recently. Data scientists have become extremely sought after, and for good reason — a skilled data scientist can add incredible value to a business. But what about data engineers? Who are they, and what do they do? A data scientist is only as good as the data they have access to. Most companies store their data in variety of formats across databases and text files. This is where data engineers come in — they build pipelines that transform that data into formats that data scientists can use. Data engineers are just as important as data scientists, but tend to be less visible because they tend to be further from the end product of the analysis. A good analogy is a race car builder vs a race car driver. The driver gets the excitement of speeding along a track, and thrill of victory in front of a crowd. But the builder gets the joy of tuning engines, experimenting with different exhaust setups, and creating a powerful, robust, machine. If you're the type of person that likes building and tweaking systems, data engineering might be right for you. In this post, we'll explore the day to day of a data engineer, and discuss the skills required for the role. The Data Engineer Role The data science field is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it's rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas, and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn't need to know the whole spectrum of skills. In this section, we'll sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. A data engineer transforms data into a useful format for analysis. Imagine that you're a data engineer working on a simple competitor to Uber called Rebu. Your users have an app on their device through which they access your service. They request a ride to a destination through your app, which gets routed to a driver, who then picks them up and drops them off. After the ride, they're charged, and have the option to rate their driver. In order to maintain a service like this, you need: A mobile app for users A mobile app for drivers A server that can pass requests from users to drivers, and handle other details like updating payment information Here's a diagram showing the communication: As you may expect, this kind of system will generate huge amounts of data. You'll have a few different data stores: The database that backs your main app. This contains user and driver information. Server analytics logs Server access logs. These contain one line per request made to the server from the app. Server error logs. These contain all the server-side errors generated by your app. App analytics logs App event logs. These contain information about what actions users and drivers took in the app. For example, you'd log when they clicked a button or updated their payment information. App error logs. These contain information about errors in the app. Ride database. This contains information about a single ride for user/driver pair, and contains status information on the ride. Customer service database. This contains information about customer interactions by customer service agents. It can include voice transcripts and email logs. Here's an updated diagram showing the data sources: Let's say a data scientist wants to analyze a user's action history with your service, and see what actions correlate with users who spend more. In order to enable them to create this, you'll need to combine information from the server access logs and the app event logs. You'll need to: Gather app analytics logs from user devices regularly Combine the app analytics logs with any server log entries that reference the user Create an API endpoint that returns the event history of any user In order to solve this, you'll need to create a pipeline that can ingest mobile app logs and server logs in real-time, parse them, and attach them to a specific user. You'll then need to store the parsed logs in a database, so they can easily be queried by the API. You'll need to spin up several servers behind a load balancer to process the incoming logs. Most of the issues that you'll run into will be around reliability and distributed systems. For example, if you have millions of devices to gather logs from, and variable demand (in the morning, you get a ton of logs, but not as many at midnight), you'll need a system that can automatically scale your server count up and down. Running servers behind a load balancer. Servers are registered with the load balancer, and the load balancer sends traffic to them based on how busy they are. This means servers can be added or removed as needed. Roughly, the operations in a data pipeline consist of the following phases: Ingestion — this involves gathering in the needed data. Processing — this involves processing the data to get the end results you want. Storage — this involves storing the end results for fast retrieval. Access — you'll need to enable a tool or user to access the end results of the pipeline. A data pipeline — input data is transformed in a series of phases into output data. Finding bad quality rides For a more complex example, imagine that a data scientist wants to build a system that finds all rides that ended prematurely due to app or driver issues. One way to do this is to look at the customer service database to see which rides ended with issues, and analyze their language logn with some data about the ride. Before the data scientist can do this, they need a way to match up the logs in the customer service database with specific rides. As a data engineer, you'll want to create an API endpoint that allows the data scientist to query for all customer service messages related to a particular ride. In order to do this, you'll need to: Create a system that pulls data from the ride database, and figures out information about the ride, such as how long it was, and whether the destination matched the user's initial request. Combine the computed statistics on each ride with user information, such as name and user id. Extract error information from the app and server analytics logs pertaining to the user during the time period of the ride. Find all customer service queries by a user. Create some heuristic to match rides with customer service queries (a simple example is that a customer service query is always about the previous ride) Store values as needed to ensure that the API performs quickly, even for future rides. Create an API that returns all customer service messages related to a particular ride. A skilled data engineer will be able to build a pipeline that performs each of the above steps every time a new ride is added. This will ensure that the data served by the API is always up to date, and that whatever analysis the data scientist does is valid. Data engineering skills A data engineer needs to be good at: Architecting distributed systems Creating reliable pipelines Combining data sources Architecting data stores Collaborating with data science teams and building the right solutions for them Note that we didn't mention any tools above. Although tools like Hadoop and Spark and languages like Scala and Python are important to data engineering, it's more important to understand the concepts well and know how to build real-world systems. We'll continue this focus on concepts over tools throughout this series on data engineering. Data Engineering Roles Although data engineers need to have the skills listed above, the day to day of a data engineer will vary depending on the type of company they work for. Broadly, you can classify data engineers into a few categories: Generalist Pipeline-centric Database-centric Let's go through each one of these categories. Generalist A generalist data engineer typically works on a small team. Without a data engineer, data analysts and scientsts don't have anything to analyze, making a data engineer a critical first member of a data science team. When a data engineer is the only data-focused person at a company, they usually end up having to do more end-to-end work. For example, a generalist data engineer may have to do everything from ingesting the data to processing it to doing the final analysis. This requires more data science skill than most data engineers have. However, it also requires less systems architecture knowledge -- small teams and companies don't have a ton of users, so engineering for scale isn't as important. This is a good role for a data scientist who wants to transition into data engineering. When our hypothetical Uber competitor, Rebu, is small, a data engineer might be asked to create a dashboard that shows the number of rides taken for each day in the past month, along with a forecast for the next month. Pipeline-centric Pipeline-centric data engineers tend to be necessary in mid-sized companies that have complex data science needs. A pipeline-centric data engineer will work with teams of data scientists to transform data into a useful format for analysis. This entails in-depth knowledge of distributed systems and computer science. As Rebu grows, a pipeline-centric data engineer might be asked to create a tool that enables data scientists to query metadata about rides to use in a predictive algorithm. Database-centric A database-centric data engineer is focused on setting up and populating analytics databases. This involves some work with pipelines, but more work with tuning databases for fast analysis and creating table schemas. This involves ETL work to get data into warehouses. This type of data engineer is usually found at larger companies with many data analysts that have their data distributed across databases. After Rebu takes over the world, a database centric data engineer might design an analytics database, then create scripts to pull information from the main app database into the analytics database. A data warehouse takes in data, then makes it easy for others to query it. Data Engineering Skills In this post, we covered data engineering and the skills needed to practice it at a high level. If you're interested in architecting large-scale systems, or working with huge amounts of data, then data engineering is a good field for you. It can be very exciting to see your autoscaling data pipeline suddently handle a traffic spike, or get to work with machines that have terabytes of RAM. There's satisfaction in building a robust system that can work for months or years with minimal tweaking. Because data engineering is about learning to deal with scale and efficiency, it can be hard to find good practice material on your own. But don't give up hope — it's very possible to learn data engineering on your own and get a job in the field. We've recently launched our new interactive Data Engineering Path at Dataquest, designed to teach you the skills you need to become a data engineer. If you're interested, you can sign up start learning for free.