Technical Lead

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