This post is contributed by Caroline Evans, Burtch Works’ data engineering recruiting specialist.
As the data engineering field continues to evolve alongside shifts and advancements in technology, there have been some specializations emerging that focus on different backgrounds and job responsibilities.
Job titles are of course fluid and can vary greatly depending on the organization – depending on who you’re talking to, even “Data Engineer” could mean something completely different. Since many are unsure of how to define these areas, I thought it might be beneficial to share some of the common elements I’ve heard in my conversations with employers and professionals in this space.
Different Job Titles and Specializations Within Data Engineering
There are five focus areas that I hear about most often in data engineering. Below I’ve outlined the most common job titles, their typical responsibilities, and included a list of some other job titles you may hear.
The most common educational backgrounds I see for these positions are computer science, information systems, electrical engineering, or software engineering.
Big Data Engineer
Software engineering backgrounds, design and build complex data pipelines, work closely with data scientist to put code into production, they typically use a lot of tools and are expert coders using Python, Java, Scala, and/or C++. Experience in Hadoop ecosystem, Spark, AWS, etc.
Business Intelligence Engineer
Data warehousing backgrounds, typically understanding/gathering business requirements to then design, and build reporting solutions. Support the data warehouses, ETL, dashboards, and reports. Tools/Technologies: SSIS, PowerBI, Tableau, RDBMS systems, MicroStrategy
I often see lots of confusion on the difference between a Data Engineer and a BI Engineer, but typically a BI Engineer is working with smaller amounts of structured data, and comes from more of a data administration/warehousing background.
Machine Learning Engineer
Hybrid role, bridges the gap between data engineering and data science, these folks come from data engineering backgrounds, have enough experience to be proficient in both data engineering and data science. They take what a data scientist finds and gets it ready for production. They create the last part of the DS pipeline. This could be rewriting code from R/Python to Java/Scala.
Computer Vision Engineer
Software engineering background, specializing in machine learning and/or deep learning techniques in relation to object detection, pattern recognition, face recognition, object tracking, etc. They are a mix between data and machine learning engineers and use tools like Python, C++, OpenCV, MATLAB, Java, and Spark. Oftentimes have a Master’s or PhD in Computer Science.
Learn more about Computer Vision in this post on data science specializations.
Software engineering or database admin backgrounds, go-to-data management person who works closely with the business users to make sure all needs are met, extensive knowledge of databases and understanding of how data relates to business operations. They develop data architecture and work with a team of Data Engineers to execute the data strategy.
Other Data Engineering job titles may include: Hadoop Developer, BI Developer, Quantitative Data Engineer, Search Engineer, Technical Architect, Big Data Analyst, Solutions Architect, Data Warehouse Engineer, Data Science Software Engineer, ETL Developer, to name a few….
Which areas are the most in-demand right now?
Big Data Engineer and Machine Learning Engineers are by far the most desired by employers over the last year. I still see a good amount of demand for BI Engineers but I think that will change in the near future as companies continue to move out of legacy systems and into the cloud. As I’ve written before, having a strong understanding of machine learning and AI concepts/techniques is something that can really make data engineers stand out during the hiring process.
If you’re thinking about making a career move and need to get your resume into shape, I also wrote a post specifically about how data engineers can create strong resumes. Especially because there are so many technical tools involved, creating a concise document can be tricky. I also shared formatting advice for data engineering consultants and new graduates.
I hope you found this information helpful, and if you’re looking for opportunities or to hire professionals in data engineering, be sure to connect with me on LinkedIn!