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).