Senior Analytics Engineer

Asana•Vancouver, BC

3d•$146,000 - $166,000•Hybrid

About The Position

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 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. 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 your recruiter can share more about the in-office requirements.

Requirements

Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
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.
Advanced SQL and strong data modeling fundamentals: dimensional modeling, star/snowflake schemas, slowly changing dimensions, and semantic layer design.
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).
Practical experience with data quality testing and observability, schema management and data contracts, and query/model performance and cost tuning.
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.
Strong cross-functional partnership skills: requirement gathering, prioritization, documentation and enablement, driving alignment on metric definitions, and explaining technical tradeoffs to non-technical partners.
Curiosity about AI-native analytics — NL2SQL, metadata/semantic layers for self-serve, and using tools like Claude and Genie to multiply your reach rather than replace rigor.

Nice To Haves

Exposure to Unity Catalog, Looker/LookML, or reverse-ETL/activation (Salesforce, Marketo, Gainsight) is a plus

Responsibilities

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.
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.
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.
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&A to keep KPI definitions consistent where domains overlap.
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.
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.
Partner directly with Product & 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.