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 ProductBecause 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-holdingThe 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 roleThe Data Engineer III will work within an established data stack that includes:Warehousing: Snowflake as the primary warehouse; Big Query for GCP-native workloadsIngestion: Fivetran for Saa S-to-Snowflake pulls; Airflow (on AWS) for custom DAGs, Lambda-based extraction, and S3 stagingModeling: dbt for transformation and semantic layer definitionCustom compute: Databricks for bespoke modeling work that doesn’t fit cleanly into dbt/SnowflakeInfrastructure: Terraform for managing Fivetran connections, GCP infrastructure, and AWS resourcesVersion control & CI/CD: Git HubConsumption: Tableau dashboards; internal tools built ontop of semantic data layersBuild 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 casesDesign and maintain dbt models that feed the team’s semantic layer,



ensuring they are tested, documented, and reusablePartner directly with stakeholders across Marketing, Finance, Sales, Legal, HR, and Product to scope data requirements and translate them into pipeline specsContribute to Terraform-managed infrastructure for Fivetran connectors, GCP resources, and AWS componentsFollow and contribute to team engineering standards — testing, CI/CD, code review, observability, and documentationIndependently resolve complex ETL and data quality issues, escalating only when architectural tradeoffs are in playMonitor pipelines for SLA compliance and participate in incident responseApply data governance practices to ensure PII is handled correctly and BU-specific compliance requirements (Finance, Legal, HR) are metCommunicate technical concepts clearly to non-technical stakeholders and advise them on what is and isn’t feasibleBenefits Perks for the Best You - We want our employees and their families to thrive. In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-beingBase Pay - We attract and retain the best talent with competitive compensation that aligns with industry benchmarks and merit programs yearly for promotions and salary increases based on performance, time in role, and position to marketBonus - When you crush it,



we like to celebrate! Bonuses are awarded annually in connection with company and individual performanceEquity - We offer each employee a generous equity award in the form of RSUs, so they can directly feel the results and success of their hard workVacation & Time Off - We know everyone’s needs are different so we offer flexible paid time off plans including an unlimited vacation option for many employeesHybrid Working Model - We offer flexible hybrid remote working models to promote equity, employee wellness, and work life balanceSolid 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 aplus Hands-on experience with a cloud data warehouse (Snowflake strongly preferred; Big Query or equivalentacceptable)Experience with AWS (Airflow, S3, Lambda) and GCP (Big Query 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#J-18808-Ljbffr

📌 Data Engineer (Toronto)
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📍 Toronto