The definition from the Fundamentals of Data Engineering, as it’s one of the most recent and complete:
Data engineering is the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engineering intersects security, Data Management, DataOps, data architecture, orchestration, and software engineering.
A Data Engineer today oversees the whole data engineering process, from collecting data from various sources to making it available for downstream processes. The role requires familiarity with the multiple stages of the Data Engineering Lifecycle and an aptitude for evaluating data tools for optimal performance across several dimensions, including price, speed, flexibility, scalability, simplicity, reusability, and interoperability.
Data Engineering helps also overcome the bottlenecks of Business Intelligence:
- More transparency as tools are open-source mostly
- More frequent data loads
- Supporting Machine Learning capabilities
Compared to existing roles it would be a software engineering plus business intelligence engineer including big data abilities as the Hadoop ecosystem, streaming, and computation at scale. Business creates more reporting artifacts themselves but with more data that needs to be collected, cleaned, and updated near real-time and complexity is expanding every day.
With that said more programmatic skills are needed similar to software engineering. The emerging language at the moment is Python which is used in engineering with tools alike Apache Airflow, dagster, Prefect as well as data science with powerful libraries.
As a data engineer, you use mainly SQL for almost everything except when using external data from an API. Here you’d use ELT tools or write some Data Pipeline with the tools mentioned above.