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Salary: CAD 106,000 - 120,000 / annual
Asana's Data Science & Analytics team is seeking a Senior Analytics Engineer to own the data foundations for a business domain end-to-end. You will sit at the intersection of Data Engineering, Analytics, and Data Science, transforming raw data into reliable, business-ready datasets that product managers, analysts, data scientists, and leaders trust and use.
Key responsibilities include:
• Own the Gold layer for a given business domain (e.g., PLG funnel, marketing attribution, revenue, NPI/AWM): Design and continuously improve curated, dimensional data models that downstream dashboards, Genie spaces, and ELT reporting depend on.
• Implement canonical business logic behind your domain's core KPIs: Translate KPIs into governed, versioned metric marts that resolve metric discrepancies definitively.
• Build and curate the semantic layer and Genie spaces powering self-serve analytics in your domain: Author metadata, documentation, and prompt/metric definitions enabling stakeholders to query governed data in plain language through Claude and Databricks Genie.
• Own the metric dictionary for your domain as a single source of truth: Define what each metric means, ownership, and location. Partner across Data Science & Analytics to maintain consistent KPI definitions where domains overlap.
• Author data contracts and SLAs at the Silver→Gold boundary: Partner with Horizontal Data Engineering on input dependencies and own data quality, freshness, and on-call support for Gold/metric-mart failures.
• Build and maintain certified, board-ready dashboards on governed Gold data: Partner with Data Science to translate insight requirements into trusted, reusable products rather than one-off builds.
• Partner directly with Product, Business, Data Science, and Engineering: Turn ambiguous questions into scalable datasets, anticipate downstream reporting impacts, and raise data-model quality standards.
The role is based in Asana's Vancouver office with an office-centric hybrid schedule. Standard in-office days are Monday, Tuesday, and Thursday, with flexibility on Wednesdays and Fridays depending on work type.
Requirements:
• 4+ years in analytics engineering, data engineering, or closely related analytics role, with a track record of independently owning data models a team relies on for decisions.
• Advanced SQL and strong data modeling fundamentals: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and semantic layer design.
• Hands-on experience with a transformation framework (dbt or equivalent), orchestration tooling (e.g., Airflow), version control (Git), and modern warehouse/lakehouse platforms (Databricks experience preferred).
• Practical experience with data quality testing and observability, schema management and data contracts, and query/model performance and cost tuning.
• Demonstrated domain fluency in at least one business area (e.g., PLG funnels, SLG pipeline, marketing attribution, Product telemetry, revenue/ARR) with judgment to translate data trust issues into specific, durable model fixes.
• Strong cross-functional partnership skills: requirement gathering, prioritization, documentation and enablement, driving alignment on metric definitions, and explaining technical tradeoffs to non-technical partners.
• Curiosity about AI-native analytics—NL2SQL, metadata/semantic layers for self-serve, and using tools like Claude and Genie to multiply reach. Exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation (Salesforce, Marketo, Gainsight) is a plus.
• Demonstrates curiosity about AI tools and emerging technologies, with willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
