A data engineer builds and maintains the pipelines, storage and tooling that move data from source systems into places where it can be trusted and used. Companies usually need one before analytics or AI work can scale.
A data engineer builds the pipelines and storage that move and prepare data, while a data scientist analyses that data and builds models to answer questions. Most teams need the first before the second can be productive.
Data engineering is the discipline of building and running the systems that collect, store, move and prepare data. Its output is reliable data that analysts, data scientists and product features can use.
A data pipeline is an automated series of steps that moves data from where it is created to where it is stored, analysed or used. Steps along the way may extract, clean, reshape or enrich it.
A data warehouse is a central database built for analysis, holding cleaned, structured data from many systems. Analysts and BI tools query it for reports, dashboards and planning.
A data lake keeps raw data of any format cheaply for later use; a data warehouse holds cleaned, structured data modelled for fast queries and reporting. Many teams run both, or a lakehouse that combines them.
A Python developer writes software in Python, a general-purpose language used for web backends, automation, data pipelines, machine learning and scripting. Before hiring one, decide which of those areas the job actually needs.
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