Data Engineer III in Toronto

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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.

What You'll Do:

• 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.

What You Bring:

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

Skills:

• Core: Python, SQL, Snowflake, dbt, Airflow, Fivetran, Databricks, AWS (S3, Lambda, Airflow), GCP (BigQuery), GitHub.
• Required: Experience with CI/CD for data pipelines; experience contributing to a team dbt project; familiarity with Terraform or another IaC tool.