Analytical Engineering Manager

<h1 data-path-to-node="3">Analytical Engineering Manager</h1> <p data-path-to-node="4">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.</p> <p id="p-rc_b89c3201101c0fcc-38" data-path-to-node="5"><span data-path-to-node="5,0">This role is based in our Vancouver <span class="citation-3894 citation-3895 citation-end-3895">office with an office-centric hybrid schedule</span></span><span data-path-to-node="5,2"><span class="citation-3892 citation-3893 citation-end-3893">. The standard in-office days are Monday, Tuesday, and Thursday</span></span><span data-path-to-node="5,4"><span class="citation-3890 citation-3891 citation-end-3891">. Most Asanas have the option to work from home on Wednesdays</span></span><span data-path-to-node="5,6"><span class="citation-3888 citation-3889 citation-end-3889">. Working from home on Fridays depends on the type of work you do and t</span><span class="citation-3888 citation-end-3888">he teams with which you partner</span></span><span data-path-to-node="5,8"><span class="citation-3887 citation-end-3887">. If you're interviewing for this role, your recruiter will shar</span>e more about the in-office requirements</span><span data-path-to-node="5,10">.</span></p> <h2 data-path-to-node="6">What you’ll achieve</h2> <ul data-path-to-node="7"> <li> <p data-path-to-node="7,0,0">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.</p> </li> <li> <p data-path-to-node="7,1,0">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.</p> </li> <li> <p data-path-to-node="7,2,0">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.</p> </li> <li> <p data-path-to-node="7,3,0">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.</p> </li> <li> <p data-path-to-node="7,4,0">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.</p> </li> <li> <p data-path-to-node="7,5,0">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.</p> </li> </ul> <h2 data-path-to-node="8">About you</h2> <ul data-path-to-node="9"> <li> <p id="p-rc_b89