Analytical Engineering Manager

The role

This manager leads a group of analytical engineers who build the foundational data layer for Asana's Data Science and Analytics function. The role sits across data engineering, analytics, and data science, and is measured by whether business stakeholders trust the numbers and can answer their own questions.

What you would do

• Hire, coach, and advance a group of analytical engineers while holding a high bar for model quality and stakeholder confidence.
• Oversee the curated gold-tier models, standard metric definitions, dashboards, and Genie workspaces that leaders depend on.
• Run recurring analytics outputs like products, each with a named owner, a refresh schedule, and a service-level target.
• Drive self-service adoption by prioritizing gold tables, governed definitions, and metadata so non-technical teams can self-answer routine questions.
• Work with data science, data platform, and business groups to define data contracts and freshness commitments at the silver-to-gold handoff.
• Decide which efforts to build, automate, or retire, and track prioritization, operating cost, and throughput as core measures.
• Surface build-versus-buy and deprecation tradeoffs openly rather than letting low-value work quietly consume team capacity.

What they asked for

• At least three years in a leadership role over technical analysis or data-building staff, with clear progress toward managing people.
• Expert-level SQL, dimensional and semantic modeling, hands-on work with dbt or comparable transformation tooling, and familiarity with modern lakehouse or warehouse platforms.
• Proven delivery of governed core tables, unified metric definitions, and semantic layers that cut down on one-off requests and built stakeholder confidence.
• Skilled at aligning senior cross-functional and executive partners by turning vague business asks into plans and agreeing on what metrics mean.
• Able to explain technical tradeoffs clearly to non-technical listeners.
• Good judgment on ranking work, retiring low-impact efforts, and controlling data spend and ongoing operating load.
• Interest in AI-driven analytics such as natural-language-to-SQL, self-service semantic layers, and assistants like Claude or Genie used to extend team reach without weakening rigor.

Nice to have

• Hands-on familiarity with Unity Catalog, LookML-based Looker, or pushing modeled data back into operational tools such as Salesforce, Marketo, or Gainsight.