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Engineering · Bangalore, IND · Full-time

Product Engineer

A front-office engineering role, performed on behalf of our clients. Build and operate the systems hedge funds run their investment process on — turning ambiguous requests from portfolio managers, analysts, and operations teams into production data pipelines, analytics, and APIs you stand behind. Correctness matters as much as velocity, and we expect both. You won't own the user interface; we have a dedicated UI team for that.

What you’ll do

  • Turn ambiguous requests from client investment and operations staff into specifications, and then into production systems
  • Design and maintain high-performance APIs in Python (FastAPI or similar) that power client-specific data access and analytics
  • Build and operate reliable ingestion and transformation pipelines for market, position, and reference data using an orchestrator (Airflow, Dagster, or Prefect)
  • Architect analytical and transactional data models that hold up as the business changes, and maintain the metric definitions and semantic layer that keep the same number consistent across a dashboard, a report, and an agent's answer
  • Implement analytics layers for performance, exposure, and risk using linear algebra and timeseries operations (Pandas, Polars)
  • Build reconciliation, validation, and monitoring, and trace discrepancies back through the pipeline to source data — a wrong number reaching a portfolio manager is an incident, not a bug
  • Work directly with client investment, research, and operations teams, alongside engineers across Bangalore and New York, to gather requirements, iterate, and support live workflows

What we’re looking for

  • 3–10 years of engineering experience, with at least 2 years in or adjacent to financial markets
  • Working knowledge of investment data models — positions, securities, transactions, P&L, exposure, returns — and how a front-office team actually uses them, from a hedge fund, asset manager, proprietary trading desk, sell-side desk, fund administrator, or a vendor serving them
  • A degree in computer science, engineering, mathematics, or a related quantitative field, or equivalent practical experience
  • Strong Python and SQL, with hands-on experience building APIs (FastAPI, Flask, or Django) and a solid grasp of RESTful and secure API design
  • Production experience with data pipelines and an orchestration tool (Airflow, Dagster, Prefect)
  • Experience with analytical databases and warehouses (Snowflake, ClickHouse, DuckDB, SQL Server, Databricks) and deliberate data modeling, not incidental SQL
  • Experience with analytical libraries (Polars, Pandas) for computation-heavy workloads, plus data quality frameworks, Docker, and a cloud platform (Azure, AWS, or GCP)
  • Comfort with ambiguity, the judgment to ask the right question of a non-technical stakeholder, and the communication skills to translate technical concepts for investment and operations staff
  • Strongly preferred: portfolio analytics, risk platforms, or fund accounting systems (factor risk, attribution, reconciliation); AI development workflows and agentic frameworks (Claude Code, LangGraph, MCP servers, RAG); event-driven and streaming architectures; dbt and local analytics with DuckDB; performance optimization on large timeseries workloads