Data Engineer
Design, build, and maintain data pipelines and infrastructure at scale
Required Courses (Core Skills)
SQL
Master database design and complex queries for pipelines.
Python
Build data pipelines, scripts, and automation tools.
Git & GitHub
Collaboration and version control for infrastructure-as-code.
Google BigQuery
Cloud data warehousing and large-scale data processing.
dbt (Data Build Tool)
Orchestrate and test data transformations.
Apache Spark / PySpark
Distributed computing and large-scale data processing.
Recommended Courses (Boost Your Skills)
Skills You'll Gain
- Design and build scalable data pipelines
- Deploy and maintain data warehouses
- Implement ETL/ELT processes
- Optimise data systems for performance and cost
- Work with cloud data platforms (AWS, GCP, Azure)
- Ensure data quality and reliability at scale
A Day in the Life of a Data Engineer
You start your day reviewing pipeline logs from the night's automated data ingestion. One pipeline failed due to a schema change in an upstream system — you investigate, fix the ingestion logic, and add tests to catch similar issues. During standup, the team discusses a new requirement: millisecond-latency event streaming. You sketch an architecture using Kafka and Spark Streaming. Later, you review a pull request from a junior engineer implementing a new dbt transformation. You approve it with comments, then deploy the updated pipeline to production. By end of day, you've increased the platform's reliability and capacity to handle 10x more data volume. Your infrastructure enables analysts and scientists to do their best work.
Salary & Job Market
Junior Engineer
£50,000
0-2 years. SQL, Python, pipeline basics.
Senior Engineer
£70,000
3-5 years. Cloud architecture, Spark, optimization.
Engineering Lead
£95,000+
5+ years. Team leadership, system design.
Ready to Start Your Data Engineering Journey?
Get all 14 courses including every course on this pathway.