About the role
Responsibilities:
- Desk-Facing Delivery: Turn ambiguous requests from client investment and operations staff into specifications and then into production systems.
- Backend Services: Design and maintain high-performance APIs using Python (FastAPI or similar) that power client-specific data access and analytics.
- Data Pipelines: Build and operate reliable ingestion and transformation pipelines for market, position, and reference data using an orchestrator (Airflow, Dagster, or Prefect).
- Data Modeling: Architect analytical and transactional data models that hold up as the business changes, the trusted foundation for dashboards, reports, and AI agents.
- Financial Semantics: Implement analytics layers for performance, exposure, and risk calculations using linear algebra and time-series operations (Pandas, Polars).
- Data Quality: Build reconciliation, validation, and monitoring. A wrong number reaching a portfolio manager is an incident, not a bug.
- Governed Metrics: Maintain metric definitions and a semantic layer so the same number means the same thing in a dashboard, a report, and an agent's answer.
- Numerical Debugging: Trace a discrepancy back through the pipeline to source data to debug the number, not just the stack trace.
- Client Collaboration: Work directly with client investment, research, and operations teams, alongside engineers across Bangalore and New York, to gather requirements, iterate, and support live workflows.
Requirements:
- 3-8 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.
- Direct experience at 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 skills.
- Hands-on experience building APIs using FastAPI, Flask, or Django, and a solid understanding of RESTful and secure API design.
- Production experience with data pipelines and an orchestration tool (Airflow, Dagster, Prefect).
- Experience with analytical databases and data warehouses (e. g., Snowflake, ClickHouse, DuckDB, SQL Server, Databricks), and deliberate data modeling, not incidental SQL.
- Experience with analytical libraries (Polars, Pandas) for computationally heavy workloads.
- Familiarity with data quality frameworks and monitoring.
- Hands-on experience with Docker and containerized development.
- Exposure to cloud platforms (Azure, AWS, or GCP) and cloud-native architectures.
- Comfort with ambiguity and the judgment to ask the right question of a non-technical stakeholder.
- Excellent communication skills for translating technical concepts to investment and operations staff.
- Strongly Preferred Experience with portfolio analytics, risk platforms, or fund accounting systems; comfortable with complex models such as factor risk, attribution, or reconciliation.
- Familiarity with AI development workflows and agentic frameworks (Claude Code, LangGraph, MCP servers, RAG).
- Familiarity with event-driven and streaming architectures for real-time and batch data processing.
- Hands-on exposure to dbt and local analytics with DuckDB.
- Performance optimization on large time series workloads.
- Track record of shipping in fast-paced, client-facing environments.
- Comfortable reading frontend code and working alongside our UI team; you won't own the UI.