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Salary: CAD 157,000 - 178,000 / annual
Asana is seeking an Analytical Engineering Manager to lead the Data Science & Analytics team's analytical engineering function. In this role, you will manage a team of Analytical Engineers responsible for building and maintaining Asana's data foundations: the Gold layer, canonical metrics, certified dashboards, and semantic layer that enable trustworthy decision-making across the organization.
You will sit at the intersection of Data Engineering, Analytics, and Data Science, accountable for ensuring business stakeholders trust the data in your team's domains and can self-serve answers without routing through your team. This includes leveraging AI-powered tools like Claude and Databricks Genie to multiply your team's reach.
Key responsibilities include:
- Lead, grow, and develop a team of Analytical Engineers: own hiring, coaching, performance management, career growth, and set high standards for data-model quality and stakeholder trust
- Own the Gold layer and semantic-layer strategy across your team's domains (e.g., PLG, marketing, revenue, NPI/AWM), ensuring curated data models, canonical metrics, dashboards, and Genie spaces the business depends on
- Treat every recurring insight as a product with an owner, cadence, and SLA; build a catalog of trusted, versioned data products instead of one-off rebuilds
- Drive self-serve enablement by prioritizing Gold tables, governed metric definitions, and metadata that make Claude + Databricks Genie trustworthy
- Partner with Data Science, Data Engineering, Data Infrastructure, and business teams to author data contracts and SLAs at the Silver→Gold boundary; decide what to build, automate, or sunset
- Manage prioritization, run-rate, and cost as first-class metrics, making explicit build-vs-buy and stop-doing trade-offs
The role is based in Vancouver with an office-centric hybrid schedule. Standard in-office days are Monday, Tuesday, and Thursday, with flexibility on Wednesdays and Fridays depending on team needs.
REQUIREMENTS:
- 3+ years managing or leading a team of analytics engineers, data engineers, or analysts, with a clear trajectory into people management
- Strong analytical-engineering technical foundation: advanced SQL, data modeling and semantic layer design, dbt or equivalent transformation framework, and modern warehouse/lakehouse platforms (Databricks preferred)
- Track record of shipping trusted data products—governed Gold tables, canonical metrics, and semantic layers—that meaningfully reduced ad-hoc work and earned stakeholder trust
- Strong stakeholder management with senior cross-functional partners and leadership: translating ambiguous business needs into roadmaps, driving alignment on metric definitions, and explaining technical tradeoffs to non-technical audiences
- Sound judgment on prioritization, sunsetting low-value work, and managing data cost and run-rate
- Demonstrates curiosity about AI tools and emerging technologies, with willingness to learn and leverage them
- Curiosity about AI-native analytics (NL2SQL, semantic layers for self-serve, Claude and Genie)
- Plus: exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation platforms (Salesforce, Marketo, Gainsight)
