Job Description
Key Responsibilities:
nPipeline 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.
nData platform — Structure and organize datasets so they can be integrated, joined, and queried effectively across the platform.
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- 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.n
- Insight & reporting (roadmap) — Enable effective querying across multiple integrated datasets to combine information and support sensible, decision-ready reporting.n
- 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.n





nKey Competencies:
nRequired Skills • 4–5+ years of hands-on data engineering experience, ideally within financial services or data intensive environments.
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- Solid experience building and orchestrating data pipelines with Apache Airflow.n
- Solid working knowledge of Apache Spark for large-scale data processing.n
- 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.n
- Track record of automating data workflows and building for reliability, monitoring, and data quality.n

nNice to Have.
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- Exposure to AI/LLM engineering: document processing at scale, embeddings, and vector databases (e.g. pgvector, Pinecone, Weaviate, or similar).n
- Experience building or supporting retrieval-augmented generation (RAG) workflows.n
- Familiarity with cloud dat

📌 Technical Lead (Pune)
🏢 Acuity Analytics
📍 Pune