Senior Analytical Engineering Manager

Asana's Data Science team helps us fulfill our mission by informing strategy, defining success metrics, and identifying new ways to deliver user value . Data scientists are at the crux of deepening our understanding of the customers and driving more business outcomes by leveraging experimentation, causal inference, statistical and machine learning techniques, and data storytelling . 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 questi ons without routing through your team. This role is based in our Warsaw 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 . We offer a Contract of Employment (UoP) for our employees in Poland.

What you’ll achieve

Lead, grow, and develop a team of Analytical Engineers: Own hiring, coaching, performance, and career growth, setting a high bar for data-model quality and stakeholder trust.

Own the Gold layer and semantic-layer strategy across your team's domains (e.g. PLG, marketing, revenue, NPI/AWM), taking accountability for curated data models, canonical metrics, dashboards, and Genie spaces.

Treat every recurring insight as a product with an owner, a cadence, and an SLA, building a catalog of trusted, versioned data products instead of one-off rebuilds.

Drive self-serve enablement by prioritizing Gold tables, governed metric definitions, and metadata that make Claude and Databricks Genie trustworthy for stakeholders.

Partner with Data Science, Data Engineering, Data Infrastructure, and business teams to author data contracts and SLAs at the Silver→Gold boundary, deciding what to build, automate, or sunset.

Manage prioritization, run-rate, and cost as first-class metrics, making explicit build-vs-buy and trade-off decisions to protect team capacity.

Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making .

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