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 organize 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, prioritise 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 data intensive environments.
• Strong experience building and orchestrating data pipelines with Apache Airflow. • Solid working knowledge of Apache Spark for large-scale data processing.
• Strong Python and SQL skills, with a focus on clean, maintainable, production-quality code. Experience designing data models and organizing datasets for downstream integration and querying.
• Track record of automating data workflows and building for reliability, monitoring, and data quality.
Nice to Have.
• Exposure to AI/LLM engineering: document processing at scale, embeddings, and vector databases (e.g. pgvector, Pinecone, Weaviate, or similar).
• Experience building or supporting retrieval-augmented generation (RAG) workflows.
• Familiarity with cloud data platforms and modern data stack tooling.
• Prior experience supporting investment management, hedge fund, or capital markets data environments.
• Experience working as a dedicated remote resource embedded with a client team