Seeking a hands-on Data Engineer to design, build, and automate data pipelines that bring new datasets onto the platform quickly and reliably as they are identified by the investment team
Key priority is core data engineering: ingesting, transforming, and organising data so it lands in the right place, in the right shape. Over time, the role will extend into the firm's AI roadmap — preparing large document sets for retrieval-augmented generation (RAG), managing vector databases, and enabling effective querying across integrated datasets to generate insight and reporting.
This is not a fixed-scope project role. The engineer will operate as a responsive, standing capability — picking up current pipeline builds as analysts surface new data requirements, while maintaining and improving what is already in place.
Key Responsibilities
Pipeline development — Design, build, and maintain robust data pipelines to ingest new datasets onto the firm's data platform as demand arises from the investment and analyst teams.
Automation — Automate ingestion, transformation,
and data-quality workflows to reduce manual effort and speed up onboarding of new sources.
Data platform — Structure and organise datasets so they can be integrated, joined, and queried effectively across the platform.
AI enablement (roadmap) — Process large volumes of documents into formats suitable for AI consumption; store and index content in vector databases; support retrieval workflows (RAG) that surface the right information reliably.
Insight & reporting (roadmap) — Enable effective querying across multiple integrated datasets to combine information and support sensible, decision-ready reporting.
Stakeholder collaboration — Work directly with the hiring manager and analysts to scope new requirements, prioritize the pipeline backlog, and respond quickly as new needs emerge.
Key competencies
Required Skills
4–5+ years of hands-on data engineering experience, ideally within financial services or