Lead Quantitative Snowflake Developer

Lead Quantitative Snowflake Developer (Hybrid)

Location: Montreal, QC

Onsite: 4 days a week for the first 90 days, then moves to onsite 3 days after.

Position Overview

We are seeking a Lead Quantitative Snowflake Developer to design, build, and optimize data platforms that power quantitative analytics and trading research. You will lead the development of scalable Snowflake-based data architectures, implement robust ETL/ELT pipelines using dbt, and write production-quality Python and SQL to support time-series and event-driven datasets. The role combines technical ownership, hands-on engineering, and collaboration with quantitative researchers to deliver reliable, high-performance data products for analytics and modeling.

Key Responsibilities

• Lead design and implementation of Snowflake data architectures to support quantitative analytics, ensuring scalability, security, and cost-efficiency.
• Develop, maintain, and optimize ELT/ETL pipelines using dbt and SQL to transform raw market, reference, and event data into clean analytical datasets.
• Write production-grade Python and SQL for data ingestion, transformation, validation, and orchestration; implement robust testing and monitoring.
• Collaborate closely with quantitative researchers, data scientists, and engineers to translate analytical requirements into data models and pipelines.
• Optimize query performance, clustering, micro-partitioning, and storage strategies in Snowflake to meet low-latency analytics needs.
• Establish data quality, lineage and governance practices, including automated testing, documentation, and CI/CD for dbt projects.
• Mentor and lead a small team of data engineers, setting standards for best practices, code reviews, and architectural decisions.
• Build and maintain processes for handling large-scale time-series and tick-level data, including partitioning, retention, and compression strategies.
• Integrate Snowflake pipelines with orchestration tools and cloud services (e.g., Airflow, Prefect, AWS/GCP/Azure) to enable reliable job scheduling and alerting.
• Drive cross-functional initiatives to improve platform reliability, observability, and cost control, and support ad-hoc analysis and performance troubleshooting.

Qualifications

• Bachelors or Masters degree in Computer Science, Engineering, Mathematics, Statistics, Finance, or a related field.
• 5+ years of experience building data platforms and pipelines, with at least 3 years of hands-on experience in Snowflake production environments.
• Expert-level SQL skills and proven experience designing complex, performant analytical queries and schemas.
• Strong Python development experience for data engineering tasks, including libraries such as Pandas, NumPy, and standard testing frameworks.
• Deep experience with dbt for data transformations, models, testing, documentation, and CI/CD workflows.
• Demonstrated experience optimizing Snowflake performance (clustering, partitioning, caching, resource monitors) and managing costs.
• Experience working with time-series or high-frequency datasets is highly desirable (nice-to-have: familiarity with time-series platforms like KDB, TimescaleDB, InfluxDB, or specialized tick-data stores).
• Familiarity with cloud platforms (AWS, GCP, or Azure), containerization, orchestration (Airflow/Prefect), and version control (Git).
• Strong communication skills and experience collaborating with quantitative teams; ability to translate business and research requirements into technical solutions.
• Leadership experience mentoring engineers, driving standards, and managing delivery of complex projects.

Benefits

• Hybrid-remote work schedule (4 days on-site)
• Unlimited vacation
• Personal days off
• Annual bonus program
• Career training program
• Retirement plan with company match
• Health insurance
• Public transportation benefits

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Lauren Formby - Director of Recruiting

This is a new role.

This job was first posted by CyberCoders on 06/07/2026 and applications will be accepted on an ongoing basis until the position is filled or closed.

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