Senior Analytics Engineer

<h1 data-path-to-node="7">Senior Analytics Engineer</h1> <p id="p-rc_daf644fb847abe63-64" data-path-to-node="8"><span data-path-to-node="8,0">The Data Science &amp; 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</span><span data-path-to-node="8,2">. As a Senior Analytical Engineer, you sit at the intersection of Data Engineering, Analytics, and Data Science, and you own the data foundations for a business domain end to end. Your mandate is to turn raw data into reliable, business-ready datasets that PMs, analysts, data scientists, and leaders actually trust and use — and to define the business logic and metric standards that make AI-powered self-serve trustworthy. You consume governed Silver tables and produce the Gold layer and semantic layer beneath Asana's most important metrics, dashboards, and Genie spaces.</span>&nbsp;</p> <p id="p-rc_daf644fb847abe63-65" data-path-to-node="9"><span data-path-to-node="9,0">This role is based in our Vancouver office with an office-centric hybrid schedule</span><span data-path-to-node="9,2">. The standard in-office days are Monday, Tuesday, and Thursday</span><span data-path-to-node="9,4">. Most Asanas have the option to work from home on Wednesdays</span><span data-path-to-node="9,6">. Working from home on Fridays depends on the type of work you do, and your recruiter can share more about the in-office requirements</span><span data-path-to-node="9,8">.</span>&nbsp;</p> <p id="p-rc_daf644fb847abe63-66" data-path-to-node="11"></p> <h3 data-path-to-node="12"><strong data-path-to-node="12" data-index-in-node="0">What you’ll achieve</strong></h3> <p id="p-rc_daf644fb847abe63-67" data-path-to-node="13"></p> <ul data-path-to-node="14"> <li> <p data-path-to-node="14,0,0">Own the Gold layer for a given business domain (e.g., PLG funnel, marketing attribution, revenue, NPI/AWM): Design and continuously improve the curated, dimensional data models that downstream dashboards, Genie spaces, and ELT reporting depend on.</p> </li> <li> <p data-path-to-node="14,1,0">Implement the canonical business logic behind your domain's core KPIs: Translate KPIs into governed, versioned metric marts that resolve "this number doesn't match" disputes for good.</p> </li> <li> <p data-path-to-node="14,2,0">Build and curate the semantic layer and Genie spaces that power self-serve in you