Data Engineer skills

Essential skills

SQL: Employers check whether you can write queries to join, filter, aggregate and validate data, often through a technical test.
Programming: Employers look for proficiency in a language used for data work, such as Python or Scala, and may ask you to solve a coding problem or discuss a project.
Data modelling: Employers assess whether you can shape data for reliable reporting and analysis, often by asking you to design tables or explain a schema.
ETL/ELT and pipelines: Employers check whether you can move, transform and load data reliably, usually through questions about pipeline design or a project walkthrough.
Data quality: Employers look for ways to detect and prevent errors, which they may assess through a scenario or by reviewing your testing approach.
Cloud and distributed systems basics: Employers may ask how you would store and process data at scale, or discuss services and tools you have used.

Orchestration: Employers check whether you can schedule and monitor dependent tasks, often by asking how you handle failures and retries.
Batch and streaming processing: Employers may ask you to compare the approaches and choose one for a particular use case.
Data warehouses and lakehouses: Employers assess your understanding of storage, partitioning and query performance through design questions or experience examples.
Performance and cost optimisation: Employers look for practical ways to improve efficiency, often by asking how you would diagnose a slow or costly workflow.
Security and governance: Employers check whether you understand access control, sensitive data handling and documentation, often through scenario questions.
Testing and deployment practices: Employers may review how you test, version and release data code through a project discussion or practical exercise.
Tool-specific experience: Employers check whether you can use the tools in the job description, usually by asking about hands-on work rather than familiarity alone.

Communication: Employers check whether you can explain technical choices clearly in interviews and project discussions.
Problem-solving: Employers assess how you break down unfamiliar issues through practical exercises and follow-up questions.
Collaboration: Employers look for examples of working with analysts, scientists or software teams, often through behavioural questions.
Attention to detail: Employers may assess this through code review, data validation tasks or questions about preventing mistakes.
Adaptability: Employers check how you respond to changing requirements, often by asking for an example of adjusting a solution.
Ownership: Employers look for evidence that you monitor and improve systems you build, which may come up in project walkthroughs.

Built and maintained data pipelines that transformed source data into validated, analysis-ready tables.
Improved query or pipeline performance by identifying bottlenecks and changing the data model or processing steps.
Added automated checks and clear documentation to make data workflows easier to monitor and maintain.

Practise SQL and programming with small projects that include messy, real-world-style data.
Build an end-to-end pipeline: ingest data, transform it, test it, and document how it works.
Learn the fundamentals of data modelling, orchestration, cloud storage and warehouse performance.
Add validation, logging and failure handling to projects so they are more reliable.
Practise explaining your design choices, trade-offs and results in clear, non-technical language.
Review job descriptions for recurring tools and skills, then focus your learning on the gaps that matter for your target roles.

See which skills you still need for your target role.