What is a data engineer?
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, AI platforms, and other data consumers. Their primary responsibility is to make data available, accessible, and secure to stakeholders.
This IT role requires a significant set of technical skills, including deep knowledge of SQL database design and multiple programming languages. Data engineers also need communication skills to work across departments and to understand what business leaders want to gain from the company’s large datasets. They’re often responsible for building algorithms to access raw data, too, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.
In addition, data engineers must know how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders. Depending on the organization, they may be responsible to communicate data trends as well. Larger organizations often have multiple data analysts or scientists to help understand data, whereas smaller companies might rely on a data engineer to work in both roles.
As enterprises embark on their AI-driven transformation initiatives, data engineers are also key to ensure their organizations have the data they need to power original model development, fine-tuning, RAG embedding, and other data-hungry AI deployment strategies.
The data engineer role
The role of the data engineer has evolved over time and continues to change.
“We started out with standard batch processing and relational databases,” says Bhrugu Pange, the MD who leads the technology services group at AArete, a management consulting firm.
Then there was a shift to provide data for basic analytics and reports, with a new focus on unstructured data, he says. This, in turn, evolved into real-time streaming. “Lots of data coming in fast and furious, and we need complex data processing,” he says.
Today, he adds, data engineers have to be aware of all the ways data will be consumed by humans, machines, ML models, and gen AI applications. “And they have to think about scalability and the best way to distribute the computing to get us the fast data we need,” he says. Plus, they have to be aware of the costs involved since cloud can be extremely expensive if it’s not used right.
Finally, with today’s AI applications, data engineers need to understand how to get data into a pipeline appropriate to ingest into LLMs for training, fine-tuning, and RAG embedding. “Data engineers need to be versatile,” he says.
R Ravi, professor of operations research and computer science at Carnegie Mellon University, says we’ll have a lot of grunt work to do when it comes to modernizing enterprise data platforms to get them ready for AI. They might also have to deal with new security and compliance implications.
“An important part of becoming an AI data engineer is to ask questions about ethics,” he says. “When I supply this data to this set of users, is it okay for them to see it? The data engineer is the first line of control to establish these boundaries.”
According to Dataquest, there are eight data engineering jobs in top demand now, based on industry trends.
- Data Engineer: Designs and maintains the systems that allow companies to collect, process, and analyze information.
- Big Data Engineer: Specializes in designing and managing large-scale data systems.
- Machine Learning Engineer: Specializes in deploying and managing ML models in production environments, and ensures ML models are smoothly integrated into larger systems.
- Data Architect: Creates the blueprint that guides a company’s overall data strategy and infrastructure.
- Cloud Data Engineer: Maximizes the cloud’s flexibility, speed, and efficiency to handle huge amounts of data.
- ETL Developer: Focuses specifically in extracting data from multiple sources, reshaping it to meet business needs, and loading it into warehouses.
- Data Operations Engineer (DataOps): Manages and optimizes data pipelines, focusing on automating processes and improving data quality.
- AI Data Engineer: Specializes in building infrastructure needed to deploy and scale ML and gen AI models.
Data engineer job description
Data engineers are responsible for building tools to access raw data, but they manage and organize that data, too, while keeping an eye out for trends or inconsistencies that could impact business goals. It’s a highly technical position, requiring experience and skills in areas such as programming, mathematics, and computer science. But data engineers also need soft skills to communicate data trends to others in the organization, and to help the business make use of the data it collects. Indeed says some of the most common responsibilities for a data engineer include:
- Assembling large, complex sets of data that meet business requirements
- Identifying, designing, and implementing internal process improvements including re-designing infrastructure for greater scalability and optimizing data delivery
- Building required infrastructure for extraction, transformation, and loading of data from various data sources
- Building analytical tools to use data pipelines
- Working with data, design, product and executive teams, and assisting them with data-related technical issues
- Working with executive, product, data, and design teams to support their data infrastructure needs
Data engineer vs. data scientist
Data engineers and data scientists often work closely together but serve very different functions. While data engineers develop, test, and maintain data pipelines and data architectures, data scientists tease out insights from massive amounts of structured and unstructured data, and develop ML and AI models.
Data engineer salary
Glassdoor finds that the median annual salary for a data engineer is $134,000, with a reported salary range of $111,000 to $164,000 depending on skills, experience, and location. Senior data engineers salaries range between $120,000 and $236,000, while lead data engineer salaries range from $168,000 to $252,000. Here’s what some of the top tech companies pay their data engineers per year, according to Glassdoor:
- Meta, $265,000
- Google, $242,000
- Apple, $235,000
- Cisco Systems, $234,000
- Microsoft, $196,000
- Amazon, $192,000
Then the US Bureau of Labor Statistics says this job category is expected to grow at 9% a year through 2033, which, the bureau adds, is much faster than average.
Data engineer skills
Coursera suggests acquiring solid programming skills, statistics knowledge, analytical skills, and an understanding of big data technologies to start a career in data engineering. Knowledge of distributed systems like Hadoop and Spark, as well as cloud computing platforms such as Azure and AWS, is useful, as well as strong programming skills in at least one programming language like Java, Python, or Scala. Coursera also recommends good knowledge of relational databases or NoSQL databases like MongoDB or Cassandra, and a strong understanding of ML principles, statistics, algorithms, and math concepts.
The skills on your résumé might impact your salary negotiations — in some cases by more than 15%. According to data from PayScale, the following data engineering skills are associated with a significant boost in reported salaries:
- JavaScript: +22%
- MapReduce: +21%
- Oracle: +20%
- Perl: +18%
- Amazon Redshift: +16%
- Apache Cassandra: +13%
- Django: +11%
- Project Management: +10%
- MySQL: +10%
Data engineer certifications
As the data engineer job gains importance for companies of all sizes, more organizations are offering certifications.
Top certifications include:
For more on these and other related certifications, see Top 11 data engineer and data architect certifications.
Becoming a data engineer
Many data engineers start as software engineers or business intelligence analysts before transitioning into data engineering. Data engineers typically have a background in computer science, engineering, applied mathematics, or any other related IT field. Because the role requires heavy technical knowledge, aspiring data engineers might find that a bootcamp or certification alone won’t cut it against the competition. Most data engineering jobs require at least a relevant bachelor’s degree in a related discipline, says PayScale. A bachelor’s degree in computer science is common.
You’ll need experience with multiple programming languages, including Python and Java, and knowledge of SQL database design. If you already have a background in IT or a related discipline such as mathematics or analytics, a bootcamp or certification can help tailor your résumé to data engineering positions. For example, if you’ve worked in IT but haven’t held a specific data job, you could enroll in a data science bootcamp or get a data engineering certification to prove you have the skills on top of your other IT knowledge.
If you don’t have a background in tech or IT, you might need to enroll in an in-depth program to demonstrate your proficiency in the field, or invest in an undergraduate program. If you have an undergraduate degree but it’s not in a relevant field, you can always look into master’s programs in data analytics and data engineering.
Ultimately, it’ll depend on your situation and the types of jobs you have your eye on. Take time to browse job openings to see what companies are looking for, which will give you a better idea of how your background can fit into that role.
The impact of AI on data engineer careers
Many low-level data engineer tasks, such as generating SQL queries, can already be handled by AI — and data platform providers are rapidly adding AI-powered features.
“A prompt can do a lot of the hard work for you,” says Daniel Avancini, chief data officer at Indicium, a data services and consulting firm. “In fact, a lot of the work is already being done by AI. In some areas, 20% of everything new is being built with AI.”
That means there’ll be less demand for junior data engineers, he says, but more demand for senior professionals. That’s because data engineers will now need to understand more complex areas of data engineering, including being able to deal with data lineage, governance, and hard-to-diagnose problems that might show up in data architectures and pipelines. This trend is already showing up in the numbers as Payscale finds that salaries for entry level data engineers are down 19%, while salaries for experienced data engineers are up 32%.
“They’ll get rid of entry level engineers and the more experienced ones will have five to six times higher productivity because they’ll ask AI to do it for them,” he says.
Sunil Kalra, practice head for data engineering at LatentView Analytics, says he’s seeing this happen in real life. “Previously, if a large enterprise had Hadoop and wanted to migrate to a data lake, it was a monumental effort that took up to three years,” he says. Gen AI can already cut around 20% off that time, he says. “But you still have to test out what the gen AI is giving you.”
Read More from This Article: What’s a data engineer? An analytics role in high demand
Source: News