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Data Engineering interviews. 13 titles, each with its own model.

Each page shows the competency model, the questions that test it, what changes with seniority, and where candidates lose the interview.

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Every Data Engineering title

13 titles
Data EngineerBuilds and maintains the pipelines, warehouses and platforms that move and transform data so analysts and models can use it.Senior Data EngineerDesigns data architectures and complex pipelines, sets engineering standards and mentors other data engineers.Junior Data EngineerBuilds and supports data pipelines and warehouse tables under guidance while learning SQL, cloud tooling and data modelling.Analytics EngineerTransforms raw data into clean, tested, well-documented models in the warehouse so analysts can trust and reuse them.Data ArchitectDesigns how data is structured, stored and flows across an organisation, setting the blueprint that engineers build to.SQL DeveloperWrites and tunes SQL queries, stored procedures and database objects that power applications, reports and integrations.ETL DeveloperBuilds extract, transform and load processes that move data between systems reliably and on schedule.Data Warehouse DeveloperDesigns and builds data warehouse schemas and loading processes that consolidate data for reporting and analytics.Big Data EngineerBuilds distributed data processing systems with tools like Spark, Hadoop and Kafka to handle very large or streaming datasets.Data Platform EngineerBuilds and runs the shared data platform, tooling and infrastructure that data engineers, analysts and scientists depend on.Data Engineering ManagerLeads a data engineering team, owning the data platform roadmap, delivery and the reliability of pipelines.Data ModellerDesigns logical and physical data models that define how business entities and relationships are stored in databases.Data Migration SpecialistPlans and executes the movement of data between systems during upgrades and implementations, ensuring nothing is lost or corrupted.

What the report looks like for Data Engineering

Sample report · the format, not a result
Pipeline design & reliability4/5
Data modelling & warehousing3/5
Data quality & observability2/5
Performance & cost efficiency3/5
Platform, orchestration & software engineering practice4/5
  • The situation in one sentence: where, what was short, and by how much.
  • The decision that was yours rather than the team’s, and what you chose not to do.
  • One number or one consequence that shows it worked.

Every competency is scored out of five with a line you actually said as evidence, and the weakest answers are rewritten as structure and specifics, never a script.

What Data Engineering interviewers keep scoring

Pipeline design & reliability

Builds batch and streaming pipelines that are idempotent, restartable and observable, and designs for late data, backfills and failure from the start.

Data modelling & warehousing

Designs schemas and layered models (raw, cleaned, business) that serve analytics efficiently, evolve safely and have clear ownership and definitions.

Data quality & observability

Makes data trustworthy with automated tests, freshness and volume checks, lineage and clear SLAs, and catches problems before consumers do.

Performance & cost efficiency

Optimises storage, compute and query performance with measurement, and keeps cloud data platform costs proportionate to value.

Platform, orchestration & software engineering practice

Applies software engineering discipline to data: version control, testing, CI/CD, infrastructure as code and orchestration that other engineers can maintain.

Security, privacy & data governance

Handles personal and sensitive data lawfully and safely: access control, encryption, masking, retention and lineage that satisfy POPIA/GDPR and internal governance.

Working with data consumers & producers

Partners with analysts, scientists and source-system teams: negotiates contracts, communicates changes and outages clearly, and prioritises by consumer value.

Incident ownership & follow-through

Takes responsibility when data is wrong or late, communicates impact honestly, and fixes root causes rather than patching symptoms.

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