May 6, 2024

8 Data Engineering Jobs That Are In-Demand in 2024

Interested in a career that combines cutting-edge technology with high demand and lucrative salaries? Look no further than data engineering. In this guide, you'll:

  • Discover 8 of the hottest data engineering roles for 2024
  • 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 industries. With the right expertise, you can secure a rewarding career at the forefront of data management and analysis. Read on to explore the exciting opportunities awaiting you in this fast-growing field.

Why choose a career in data engineering?

A career in data engineering offers a unique blend of technical challenges, growth opportunities, and tangible impact on business success. The numbers speak for themselves:

  • The global big data services market is projected to hit $77.37 billion by 2024.*
  • This represents an impressive compound annual growth rate (CAGR) of 17.60%.

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 roles 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 empower 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:

  1. Data Engineer
  2. Big Data Engineer
  3. Machine Learning Engineer
  4. Data Architect
  5. Cloud Data Engineer
  6. ETL Developer
  7. Data Operations Engineer (DataOps)
  8. AI Data Engineer

In the sections below, we'll explore these roles in depth, covering key responsibilities, must-have skills, and tips for success.

1. Data engineer

Person on computer surrounded by different coding screens.

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 data analysts who interpret data or data scientists who build 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: $121,000 - $199,000/yr (*)


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

Multiple stacks of data.

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: $136,000 - $213,000/yr (*)


  • 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

Person on tablet with multiple screens of data.

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: $107,800 - $253,400/yr (*)


  • 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

A hammer, screwdriver, and drill

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: $117,000 - $148,000/yr (*)


  • 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

Multiple data screens connecting to one computer

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: $130,802 - $170,000/yr (*)


  • 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: $133,000 - $199,000/yr (*)


  • 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: $136,000 - $199,000/yr (*)


  • 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

AI Data Engineer deeply immersed in data, building AI infrastructure.

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: $114,000 - $212,000/yr (*)


  • 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 these 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. Tackle 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.

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 roles. Data engineering courses can help you start to build your skills.

How to determine if one of these data engineering roles is the right fit

While data engineering roles share some common foundations like programming, databases, and data processing, they 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 data engineering role 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.


Data engineering is positioned 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 positions, 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 role 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.

Anna Strahl

About the author

Anna Strahl

A former math teacher of 8 years, Anna always had a passion for learning and exploring new things. On weekends, you'll often find her performing improv or playing chess.