Data engineer for AI
Make data usable, traceable and available to the right people.
The outcome this role owns
Reliable data pipelines with clear ownership, access rules, quality checks and reproducible inputs for AI systems.
Four levels of maturity
Proposed framework · Evidence-based discussion · Not a validated assessment scale
Foundation
Builds a simple pipeline and documents the source, schema and expected update frequency.
Evidence to look for
A source-to-output data map
Pilot
Adds automated quality checks, access rules and reproducible dataset versions for a pilot.
Evidence to look for
A versioned dataset with failing and passing quality checks
Production
Operates pipelines with service expectations, failure alerts, lineage and recoverable backfills.
Evidence to look for
An incident record and successful pipeline recovery
Organisational
Designs shared data standards and resolves ownership and quality problems across business units.
Evidence to look for
A cross-team data contract with accountable owners
A practical work sample
Present a pipeline with duplicate records, a changing schema and inconsistent access rules. Ask for the first fixes, monitoring and a safe recovery plan.
Use synthetic or public data, explain the evaluation criteria in advance and allow reasonable adjustments. Discuss the reasoning as well as the finished artifact.
What to question
A pipeline runs, but nobody can explain which data version produced the output or who should have access.
Related market signal: ict specialists
ICT employment is a broad pool of adjacent technical skills. It includes many occupations unrelated to data engineering or AI.
Highest observed shares among covered markets, 2025. These are context, not recommended hiring locations.
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Salary context
Check source role titles carefully. Broad categories and adjacent roles are labelled.
Explore related salary evidence ↗Framework sources
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