Senior Data Engineer

The Job in short

The Data Enablement team is here to enable every team in the organisation with their data needs. Our job starts the moment that data enters our platform and ends when it reaches whoever needs it. We are a small, senior team of data and AI engineers, working across customer data, product knowledge, and the data the organisation runs on.

We bring data in, reconcile it into one version people can rely on, and make it available to the right audience. Increasingly that audience is agents as well as people, so everything we build has to work for both. Two things must hold at every step: that only the right people can see it, and that we can prove it is correct. You build the platform that makes both possible

.
As a Senior Data Engineer you own the foundation: the lakehouse, the pipelines that fill it, and the layers that serve it. We treat data as a product, so each one has a named owner, a documented contract with the teams who consume it, and stated expectations on freshness and quality. That includes Data as a Service, where internal and external teams run analytics on data held in our AI-powered Banking O

S.
This is not only a tabular data job. A large part of it is a versioned documentation corpus, kept current, deduplicated and traceable across many product versions, and much of it arrives semi-structured rather than clean. The consumers are not only dashboards either. You build and maintain the MCP tools that let AI agents query documentation, API specs and release history directly. That is a meaningful part of the role, not a side proje

ct.
We hold pipelines to the same standard as application code: tested, reviewed, deployed through CI/CD, observable in production, and promoted properly across environments. If that is already how you think about data engineering, you will recognise this team quic

kly.
Meet th

• e jobDevelop and maintain pipelines extracting data from many sources: RDBMS, Change Data Capture and streaming endpo
• ints.Process data ranging from observability metrics to bank accounts and transactions, in a highly secure and compliant ma
• nner.Transform raw data into business-ready analytical models on a Data Lakeh
• ouse.Guard data consistency, quality and end to end governance across the full lifec
• ycle.Unstructured data at scale. Ingest and maintain a large versioned documentation corpus: incremental sync, change tracking, deduplication and freshness. Much of it is public or semi-structured source material rather than clean tabular
• data.Agent-facing data tools. Build and maintain MCP tools that let AI agents query documentation, API specs and release history. A meaningful part of this
• role.Pipelines as software. GitHub Actions CI/CD, automated tests, observability and proper environment promotion across Fabric and Databricks. We automate through GitHub, and we expect the same engineering discipline from data pipelines as from application

code.
How abo

• ut youA Bachelor's or Master's degree in Computer Science, Data Science or a related
• field.5+ years of experience in data engineering, preferably Microsoft Fabric, Azure Databricks or Spark. Strong experience on another major cloud is fine if you are ready to co
• nvert.Expertise in Data Lakehouse concepts and Delta
• Lake.Working knowledge of data regulation and compliance, and the judgement to know when to involve
• Legal.Strong technical leadership. You can drive a piece of work end to end, take the calls with other value streams and stakeholders yourself, and keep it moving without a project ma
• nager.Excellent written and verbal communication skills in En
• glish.Proactive, autonomous and self-sufficient. You work independently on your own projects and take the calls with other value streams and stakeholders you
• rself.Python and SQL depth, including pipeline and query performance

work.
Strong

• plusesExperience with data contracts and making a platform other teams can self-serv
• e from.A BI layer in production, such as Po
• wer BI.Machine learning fundamentals, enough to work with AI engineers as
• a peer.Retrieval and vector search. Building or maintaining the vector and hybrid search layer that AI products
• query.Preparing data for model training or fine-tuning, including anonymisation of sensitiv
• e data.Instrumenting retrieval so its quality can be measured by someon

e else.
Our te

• ch stackLanguages: Pyth
• on, SQL.Platform: Microsoft Fabric, Azure Databricks, Delta Lake, Azure Blob, Cosmos DB, Azure Ke
• y Vault.Processing and orchestration: PySpark, Fabric pipelines and no
• tebooks.Ops: GitHub Actions, Fabric deployment pipelines, P
• ower BI.AI surface: MCP, vector and hybrid