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