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 a Senior Analytical Engineer, you sit at…
What you'll do
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.
What they're looking for
- 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.
- Demonstrates curiosity about AI tools and emerging technologies, with a: willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.
- + 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.
