Greenland Investment Management is a Mumbai headquartered global hedge fund manager managing assets in excess of USD 1 billion. We manage one of the fifteen largest dedicated commodity hedge funds globally.
We specialize in cross-market arbitrage strategies across commodities and currencies, investing globally across 40+ markets. We employ a purely quantitative approach using our proprietary big-data research systems to systematically create consistent alpha generating strategies.
Our extensive network of globally connected exchange co-located servers along with our low latency trading platform allow us to algorithmically capture these market
inefficiencies across asset classes.
About the Role
We're hiring a Quant Developer to build and own the data and research infrastructure behind our
commodities research team. You'll work across data engineering, research tooling, and trading-system
integration — building pipelines, a backtesting framework, and the tooling that turns models into
production-ready systems.
Responsibilities
Market data
- Design and maintain pipelines to ingest and process tick data across the commodities product
universe.
- Integrate additional third-party data vendors and own the maintenance of their APIs and feeds.
- Store data efficiently in DuckDB and build/maintain spread series (calendar, inter-commodity,
product).
- Ensure data quality, completeness, and reliability through monitoring and validation.
Research infrastructure
- Build the research layer and a reusable, performant backtesting framework.
- Improve the codebase so researchers use the system rather than write code — clean APIs,
sensible defaults, minimal boilerplate.
Trading system integration
- Own the workflow for setting up research models on the trading system.
- Build and maintain the scripts that push model parameters to the trading system reliably and
repeatably.
Requirements
- 2+ years as a quant developer, data engineer, or software engineer in a quant/trading
environment.
- Robust Python and SQL; production-quality, well-tested code.
- Experience building data pipelines, ideally with tick-level market data.
- Working knowledge of DuckDB (or similar columnar/analytical stores).
- Solid grasp of time-series and financial tick data (gaps, timestamps, rolls/adjustments).