The opportunity
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…
What you'll do
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.
What they're looking for
- + 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.
