Location: Montreal, Quebec
Our client is the leading global provider of multi-asset trading systems to the buy-side, sell-side and trading platforms, is accelerating the technical evolution of the Fixed Income market by developing connectivity, integration and data normalization to institutional investors, dealers and trading platforms. Providing global coverage from offices in New York, London, Paris, Frankfurt, Geneva, Hong Kong, Singapore and Tokyo.
This Role:
You will build the quantitative datasets, AI pipelines, and analytics that detect signals, identify liquidity, evaluate best execution, benchmark transaction costs, and surface alpha opportunities across equities, credit, FX, fixed income, commodities, crypto, and their derivatives.
This is big data at scale. We work with trillions of price interactions, full-depth order book history, and global multi-asset tick data — the kind of volume where every architectural decision matters.
You will:
• Own end-to-end development of scalable pipelines feeding analytics, liquidity models, best-execution evaluation, signal detection, and transaction cost benchmarks across all asset classes
• Build and maintain high-performance data applications in Python, SQL, Snowflake, dbt, and OneTick to transform and validate trillions of market and trade data points
• Construct and maintain the quantitative datasets
• Design and operate real-time time-series workflows on OneTick for tick-level analytics, intraday computation, and live signal generation
• Partner with Quant Developers and the AI Engineering team to optimize analytics infrastructure for latency, throughput, and reliability at scale
• Build agentic AI workflows using Cortex Code, Claude, and OpenAI to enhance data quality, anomaly detection, signal discovery, and quantitative research velocity
• Design Snowflake Semantic Views that make trading data discoverable and queryable by both human analysts and AI agents
• Document data methodologies clearly to support internal review and external client validation
• Mentor junior team members and help set the technical standards for the team
Must Have Skills:
• BSc in mathematics, physical sciences, computer science, or engineering, or equivalent practical experience
• 7+ years of large-scale Python development, SQL programming, and data-intensive product work in a financial context
• Strong proficiency with Snowflake and dbt
• Experience with tick-level or time-series data platforms (OneTick, kdb+, or equivalent) is a strong plus
• Familiarity with Claude, OpenAI, or comparable LLM tooling is a strong plus
• Experience leading technical projects and mentoring engineers
Nice to Have Skills:
• Working understanding of market microstructure, execution analytics, or trading data, and the appetite to go deeper
• Hands-on experience applying AI and ML to financial data problems;
