Quant Developer

COMPANY PROFILE

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.

• Strong 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).