Analytical Engineering Manager

The Data Science & Analytics team at Asana is how the company turns data into decisions — defining the questions that matter, surfacing the answers, and making sure insight is at the center of every critical product and business call. As an Analytical Engineering Manager, you lead a team of Analytical Engineers who own the data foundations for the business: the Gold layer, canonical metrics, certified dashboards, and semantic layer that make Asana's most important numbers trustworthy, and that make AI-powered self-serve through Claude and Databricks Genie actually work.

You sit at the intersection of Data Engineering, Analytics, and Data Science, and you are accountable for whether business stakeholders trust the data in your team's domains and can answer their own questions without routing through your team.

This role is based in our Vancouver office with an office-centric hybrid schedule. The standard in‑office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in‑office requirements.

• Lead, grow, and develop a team of Analytical Engineers:
  Own hiring, coaching, performance, and career growth, and set a high bar 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):
  Your team is accountable for the curated data models, canonical metrics, dashboards, and Genie spaces the business depends on.
• Treat every recurring insight as a product with an owner, a cadence, and an SLA:
  Build a catalog of trusted, versioned data products instead of one‑off rebuilds.
• Drive self‑serve enablement:
  Prioritize the Gold tables, governed metric definitions, and metadata that make Claude + Databricks Genie trustworthy, so stakeholders can answer routine questions without coming to your team.
• Partner with Data Science, Data Engineering, Data Infrastructure, and business teams to author data contracts and SLAs at the Silver→Gold boundary, and decide what to build, what to automate, and what to sunset.
• Manage prioritization, run‑rate, and cost as first‑class metrics — making explicit build‑vs‑buy and stop‑doing trade‑offs rather than letting low‑value work quietly erode the team's capacity.

• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision‑making.
• 3+ years managing or leading a team of analytics engineers, data engineers, or analysts, with a clear trajectory into people management.
• A strong analytical‑engineering technical foundation that lets you set the bar: advanced SQL, data modeling and semantic layer design, dbt or an equivalent transformation framework, and modern warehouse/lakehouse platforms (Databricks preferred).
• A 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.
• Curiosity about AI-native analytics — NL2
  
  SQL, semantic layers for self‑serve, and using tools like Claude and Genie to multiply your team's reach rather than replace rigor. Exposure to Unity Catalog, Looker/LookML, or…