Data Engineer in Toronto

The Data Engineer III is a core builder on the Strategic Partnerships team’s data platform. They design and ship ETL pipelines, semantic models, and the supporting infrastructure that powers cross-functional initiatives with Marketing, Finance, Sales, Legal, HR, and Product. Because the team operates on a project/intake basis across many business units, this role requires someone who can context-switch between domains, get up to speed on unfamiliar data quickly, and deliver pipelines that stakeholders can rely on without hand-holding.

The Data Engineer III operates with a high degree of autonomy. They own projects end-to-end — from scoping with BU stakeholders, to building and deploying pipelines, to monitoring them in production. They follow the standards set by the team and contribute back to them as they grow in the role.

The Data Engineer III will work within an established data stack that includes:

• Warehousing: Snowflake as the primary warehouse; BigQuery for GCP-native workloads
• Ingestion: Fivetran for SaaS-to-Snowflake pulls; Airflow (on AWS) for custom DAGs, Lambda-based extraction, and S3 staging
• Modeling: dbt for transformation and semantic layer definition
• Custom compute: Databricks for bespoke modeling work that doesn’t fit cleanly into dbt/Snowflake
• Infrastructure: Terraform for managing Fivetran connections, GCP infrastructure, and AWS resources
• Version control & CI/CD: GitHub
• Consumption: Tableau dashboards; internal tools built on top of semantic data layers

Key Responsibilities:

• Build and own end-to-end data pipelines across Fivetran, Airflow, dbt, and Databricks — selecting the right tool for each problem with guidance from the Senior Data Engineer on novel cases.
• Design and maintain dbt models that feed the team’s semantic layer, ensuring they are tested, documented, and reusable.
• Partner directly with stakeholders across Marketing, Finance, Sales, Legal, HR, and Product to scope data requirements and translate them into pipeline specs.
• Contribute to Terraform-managed infrastructure for Fivetran connectors, GCP resources, and AWS components.
• Follow and contribute to team engineering standards — testing, CI/CD, code review, observability, and documentation.
• Independently resolve complex ETL and data quality issues, escalating only when architectural tradeoffs are in play.
• Monitor pipelines for SLA compliance and participate in incident response.
• Apply data governance practices to ensure PII is handled correctly and BU-specific compliance requirements (Finance, Legal, HR) are met.
• Communicate technical concepts clearly to non-technical stakeholders and advise them on what is and isn’t feasible.

Qualifications:

• Solid dbt experience — has built and maintained models in a production dbt project.
• Has built something end-to-end before specializing — values broad competence paired with depth.
• Experience with orchestration tools — Airflow and Fivetran required; Dagster or equivalent a plus.
• Hands-on experience with a cloud data warehouse (Snowflake strongly preferred; BigQuery or equivalent acceptable).
• Experience with AWS (Airflow, S3, Lambda) and GCP (BigQuery and adjacent services).
• 4+ years of professional experience as a Data Engineer, with demonstrated ownership of production pipelines.
• Strong communication skills — can explain technical tradeoffs to non-technical partners and adapt style to different audiences.
• Ability to work autonomously — can take an ambiguous ask, scope it with a stakeholder, and ship a solution with minimal oversight.
• Experience with Databricks and Spark for custom modeling and transformation workloads.
• Security-first mindset; familiar with PII handling and access controls.
• Strong SQL and Python; comfortable writing production-grade code, debugging complex pipelines, and optimizing queries.