Product Engineer

Hedgineer; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Hedgineer in Bengaluru is seeking an experienced software/data engineer to design high‑performance Python APIs and data pipelines for investment analytics. You will work with client teams and engineers across Bangalore and New York to gather requirements and support live workflows.

Responsibilities include building ingestion pipelines for market data, creating robust data models, and ensuring metric consistency across dashboards, reports, and agents.

Qualifications

• 3–10 years of engineering experience, with at least 2 years in or adjacent to financial markets.
• Strong Python and SQL, with hands-on API experience (FastAPI, Flask or Django).
• Production data pipelines experience and familiarity with orchestration tools (Airflow, Dagster, Prefect).
• Experience with analytical databases and data modeling for consistent metrics across dashboards and reports.
• Comfort translating technical concepts for investment and operations staff.

Responsibilities

• Turn ambiguous client requests into specifications and production systems.
• Design and maintain high-performance Python APIs powering data access and analytics.
• Build and operate reliable ingestion and transformation pipelines for market, position, and reference data.
• Architect data models and semantic layers to keep metrics consistent across dashboards and agents.
• Implement analytics layers for performance, exposure, and risk using Python data libraries.
• Collaborate with client teams and engineers across Bangalore and New York to gather requirements and support live workflows.

Skills

Python

SQL

APIs (REST)

Data Pipelines

Timeseries Analytics

Docker

Cloud Platforms

Education

Quantitative degree or equivalent

Tools

Airflow

Dagster

Prefect

Snowflake

DuckDB

ClickHouse

Databricks

Docker

Azure/AWS/GCP

Job description

• 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