Passage helps international students navigate their journey abroad — from choosing a program and getting admitted to financing their studies and helping with their visa. You’ll own Passage’s data platform end to end. Not a greenfield project and not a legacy mess: a working, documented warehouse that the company already runs on — executive KPIs, marketing attribution, funnel analytics, and internal ops tools are all powered by it today. Own a ~100-model project (staging to intermediate to marts) built on Kimball dimensional modeling: Recent work includes applicant milestone tracking, loan-process stage modeling, and ad-click-level attribution. Executive KPIs, conversion funnels, cohort analytics, and marketing attribution across Google Ads, Meta Ads, and UTM/click-ID data. Run the pipelines. Airflow 3 orchestration with Slack alerting, data-quality tests, freshness monitoring, and backfills. Serve the data. A dedicated dbt serving layer exposed through Hasura GraphQL powers internal ops tools in the product itself; Metabase serves dashboards and self-serve queries. Keep quality and cost in check. dbt tests, per-model documentation, BigQuery partitioning/clustering, and bytes-scanned discipline. Document for humans and AI. Every model is documented, and the docs auto-sync to a companion repo built so both teammates and AI agents can query the warehouse correctly. We work AI-assisted, with coding standards written into the repos. BigQuery, dbt, Airflow 3, Debezium + Kafka Connect (GKE), Airbyte,



Fivetran, Hasura, Metabase, PostgreSQL (Django app), Docker, GCP, AI-assisted development Have 4+ years building analytics or data platforms, with expert-level SQL. Have operated an orchestrator (Airflow or similar) and are comfortable in Python. Enjoy ambiguous business-logic modeling: reverse-engineering a production schema, interviewing stakeholders, and shipping a table that settles the debate. Can own a platform solo: prioritize, communicate, and ship without a spec. CDC pipelines (Debezium, Airbyte) or Kafka Connect; BigQuery performance and cost tuning. Hasura/GraphQL, reverse ETL, or embedding warehouse data into product surfaces. Experience as an early or solo data hire at a startup. Fluency with AI-assisted development workflows. You run the platform day to day — pipelines, tests, and monitoring — and stakeholders don’t feel a handoff. You’ve shipped current marts or metrics for at least two teams, from stakeholder conversation to documented, tested tables. Data quality and freshness are visibly better: fewer surprises, faster answers. We are building a company designed to expand access to life-changing opportunities around the world. If you are excited about building systems, solving complex problems, and shaping how a company makes decisions, we would love to meet you. Note: Artificial intelligence may be used in the screening or assessment of applicants. C$100K to C$150K + Offers Equity #

📌 Senior Analytics Engineer (Toronto)
🏢 Passage
📍 Toronto