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="8,1"><sup class="superscript"></sup></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="9,1"><sup class="superscript"></sup></span><span data-path-to-node="9,2">. The standard in-office days are Monday, Tuesday, and Thursday</span><span data-path-to-node="9,3"><sup class="superscript"></sup></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="9,5"><sup class="superscript"></sup></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="9,7"><sup class="superscript"></sup></span><span data-path-to-node="9,8">.</span>&nbsp;</p> <p id="p-rc_daf644fb847abe63-66" data-path-to-node="11"><sup class="superscript"></sup> </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"><sup class="superscript"></sup> </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 your domain: Author the metadata, documentation, and prompt/metric definitions that let stakeholders query governed data in plain language through Claude and Databricks Genie.</p> </li> <li> <p data-path-to-node="14,3,0">Own the metric dictionary for your domain: a single source of truth for what each metric means, who owns it, and where to find it. Partner with peers across DS&amp;A to keep KPI definitions consistent where domains overlap.</p> </li> <li> <p data-path-to-node="14,4,0">Author data contracts and SLAs at the Silver→Gold boundary, partnering with Horizontal Data Engineering on the inputs you depend on, and owning data quality, freshness, and oncall for Gold/metric-mart failures in your domain.</p> </li> <li> <p data-path-to-node="14,5,0">Build and maintain certified, board-ready dashboards on governed Gold data, partnering with Data Science to translate insight requirements into trusted, reusable products rather than one-off builds.</p> </li> <li> <p data-path-to-node="14,6,0">Partner directly with Product &amp; Business, Data Science, and Engineering to turn ambiguous, underspecified questions into scalable datasets — anticipating downstream reporting impacts before they become incidents, and raising the data-model quality bar across the domains you touch.</p> </li> </ul> <h3 data-path-to-node="15"><strong data-path-to-node="15" data-index-in-node="0">About you</strong></h3> <p id="p-rc_daf644fb847abe63-68" data-path-to-node="16"><sup class="superscript"></sup> </p> <ul data-path-to-node="17"> <li> <p id="p-rc_daf644fb847abe63-69" data-path-to-node="17,0,0"><span data-path-to-node="17,0,0,0">Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making</span><span data-path-to-node="17,0,0,1"><sup class="superscript"></sup></span><span data-path-to-node="17,0,0,2">.</span> </p> </li> <li> <p id="p-rc_daf644fb847abe63-70" data-path-to-node="17,1,0"><span data-path-to-node="17,1,0,0">4+ years in analytics engineering, data engineering, or a closely related analytics role, with a track record of independently owning the data models a team relies on for decisions</span><span data-path-to-node="17,1,0,1"><sup class="superscript"></sup></span><span data-path-to-node="17,1,0,2">.</span> </p> </li> <li> <p data-path-to-node="17,2,0">Advanced SQL and strong data modeling fundamentals: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and semantic layer design.</p> </li> <li> <p data-path-to-node="17,3,0">Hands-on experience with a transformation framework (dbt or equivalent), orchestration tooling (e.g. Airflow), version control (Git), and modern warehouse/lakehouse platforms (Databricks experience preferred).</p> </li> <li> <p data-path-to-node="17,4,0">Practical experience with data quality testing and observability, schema management and data contracts, and query/model performance and cost tuning.</p> </li> <li> <p data-path-to-node="17,5,0">Demonstrated domain fluency in at least one business area (e.g. PLG funnels, SLG pipeline, marketing attribution, Product telemetry, revenue/ARR) and the judgment to translate "I don't trust this number" into a specific, durable model fix.</p> </li> <li> <p id="p-rc_daf644fb847abe63-71" data-path-to-node="17,6,0"><span data-path-to-node="17,6,0,0">Strong cross-functional partnership skills: requirement gathering, prioritization, documentation and enablement, driving alignment on metric definitions, and explaining technical tradeoffs to non-technic