Data engineering is the link between data science and software engineering. Data engineers often work together with data scientists and analysts and help them to do their job efficiently. Moreover they are responsible for transforming models and prototypes built by data scientists and analysts to production code. Therefore data engineers need deep knowledge about software engineering, distributed (parallel) computing and machine learning (statistics).
Common tasks for data engineers are
- Design, build, test and maintain scalable data management systems (Hadoop, SQL, NoSQL, etc.)
- Implement high-performance ML algorithms, predictive models and prototypes (Spark, Tensorflow, Scikit-Learn)
- Import and export data from several sources (streaming, batch processing, cronjobs)
- Tranform, filter and stream data between data consumers and producers (data pipeline, feature extraction)
- Integrate new software and tools into the production system (visualization, modelling, business intelligence)
- Create APIs for custom software components (REST, web services)
Differences between Data Science and Data Engineering
Data Science is the sexiest Job in the 21st century and undoubted there is a lot of truth behind that. With the improvements in computing capabilities and the enormous amount of data, there are many valuable resources hidden in data lakes. Almost every rising tech company deals with data in some way and data is a new currency. But it needs more than data scientists to raise the treasure, data has to be stored and processed in an efficient way. This is where data engineers come into play.
Like the job titles imply, a data scientist is much closer to research than a data engineer. Data scientists focus on statistical modelling and data understanding, while the engineers focus on predictive modelling and implementation. While both need to know about machine learning algorithms, the perspective is different. Data scientists look at the data from a scientific perspective, they want to identify and understand the data and extract value for business. Data engineers want to implement the algorithm efficiently and put it into a production software system.
Similarities between Data Science and Data Engineering
There is a huge overlap between those two roles and in the real world data engineers are often called data scientists. It will take more time to separate the different job roles. However, both roles need knowledge in machine learning and the statistics behind. Data scientists also need basic programming and computer science knowledge.
How to become a data engineer?
The most job offers require a masters degree in computer science or any related field. In comparison to a software engineering, data engineering needs more theoretical knowledge (machine learning, statistics, math), that is why a phd is a plus. In my personal experience i can confirm that machine learning is often not a part of the bachelor studies and even with online courses in machine learning, a masters degree is still a better way to go.