Data Engineer

Concentra is a strategy consulting firm that combines strategy, data and analytics, and AI. Most organisations run strategy, analytics and AI as three separate tracks. The analytics never rises above operational reporting, the AI stays a collection of pilots, and the strategy gets set without the evidence that would have tested it.

We do the joined‑up version — and that means the engineering we do is tied to a decision someone actually has to make, not to a ticket.

The opportunity

We are seeking an experienced Data Engineer to meet growing demand on a number of client engagements.

What you would work on

Client engagements vary, but the engineering work generally falls into:

Data foundations — designing and building pipelines, models and platforms that support decisions, reporting and regulatory obligations.

Decision‑ready data — lineage that can be traced end to end, quality monitoring, and the evidence base behind numbers a board or a regulator will ask about.

AI enablement — feature pipelines, and the data access patterns agentic systems need to work safely.

Modernisation and migration — moving clients off legacy platforms without breaking the reporting they depend on.

Capability transfer — leaving the client’s own team able to run what we’ve built.

What we are looking for

Substantial commercial experience as a Data Engineer — we are recruiting at senior and lead level, because engagements are small and client‑facing with little supervision.

Strong SQL and Python, and demonstrable experience building production pipelines at scale.

Depth in at least one modern cloud data platform: Azure (Fabric, Synapse, Data Factory), AWS, Databricks or Snowflake.

Sound data modelling judgement — you can explain why a model is shaped the way it is, not just build it.

The ability to work directly with clients: to ask the question behind the request, to build partnerships with stakeholders, and to write clearly.

Well regarded

dbt, Spark, orchestration tooling (Airflow, Dagster or equivalent).

Experience in regulated environments — banking, insurance, government, telecommunications — and comfort with privacy, retention and access obligations as design constraints rather than obstacles.

Data governance and cataloguing tooling (Microsoft Purview, Collibra, Alation).

MLOps, vector stores or RAG pipeline experience.

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