Montreal, Canada•Montreal
Remote
Senior
Full Time
15 days ago
💰$100,000 - $135,000
hybrid
Requirements
- •Strong understanding of AI-assisted development workflows with hands-on experience using tools like Cursor, Claude, OpenCode, GitHub Copilot, or ChatGPT
- •Experience with spec-driven development turning requirements into clear specs/plans and acceptance criteria
- •2+ years building and operating production data pipelines and models on Snowflake using SQL, Python, and dbt
- •2+ years hands-on experience building modular, version-controlled, and tested data models using dbt
- •2+ years experience with AWS cloud services
- •Solid understanding of data quality, lineage, validation techniques, and data governance
- •Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders and work in a distributed team
What You'll Do
- •Design, build, and evolve the lakehouse data platform with reusable models and pipelines on Snowflake and dbt, and Databricks workloads where appropriate
- •Translate product and business requirements into data models and pipelines by collaborating with PMs, business units, and engineers
- •Migrate legacy ETL processes to modern, efficient streaming and incremental pipelines using Snowflake or Databricks
- •Operate and tune Snowflake for reliability, efficiency, and cost control
- •Apply AI-assisted development tools and spec-driven workflows to design, automate, and ship data pipelines and platform
- •Facilitate scoped data onboarding and empower business unit engineers to build their own data products on the platform
- •Build and evolve data behind customer-facing products to ensure trustworthy product experiences
- •Ensure data quality through robust data governance, automated testing, validation techniques, and lineage
- •Curate rich metadata in Unity Catalog and Snowflake to support downstream consumption including AI agents and semantic layer
- •Research solutions to complex problems and lead proof-of-concepts to evaluate emerging technologies
- •Author and maintain high-quality documentation to support knowledge sharing and AI-assisted workflows
Nice to Have
- •Hands-on Databricks experience including workspaces, jobs/workflows, Spark SQL/PySpark, Delta Lake
- •Exposure to Fivetran or similar ELT connectors for source-to-warehouse ingestion and schema-evolution handling
- •Exposure to Cube.dev or similar semantic/metrics layer for governed, self-serve analytics and consistent metrics
- •Experience building and maintaining real-time data solutions using streaming platforms like Apache Kafka
Benefits
- •Performance-based bonuses
- •Full range of benefits
