Analytical Engineering Manager

What you'll achieve

Lead, grow, and develop a team of Analytical Engineers:
Own hiring, coaching, performance, and career growth, and set 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):
Your team is accountable for the curated data models, canonical metrics, dashboards, and Genie spaces the business depends on.

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

Drive self-serve enablement:
Prioritize the Gold tables, governed metric definitions, and metadata that make Claude + Databricks Genie trustworthy, so stakeholders can answer routine questions without coming to your team.

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

Manage prioritization, run‑rate, and cost as first‑class metrics — making explicit build-vs-buy and stop‑doing trade‑offs rather than letting low-value work quietly erode the team's capacity.

About you

3+ years managing or leading a team of analytics engineers, data engineers, or analysts, with a clear trajectory into people management.

A strong analytical-engineering technical foundation that lets you set the bar: advanced SQL, data modeling and semantic layer design, dbt or an equivalent transformation framework, and modern warehouse/lakehouse platforms (Databricks preferred).

A track record of shipping trusted data products — governed Gold tables, canonical metrics, and semantic layers — that meaningfully reduced ad‑hoc work and earned stakeholder trust.

Strong stakeholder management with senior cross‑functional partners and leadership: translating ambiguous business needs into roadmaps, driving alignment on metric definitions, and explaining technical tradeoffs to non‑technical audiences.

Sound judgment on prioritization, sun setting low‑value work, and managing data cost and run‑rate.

Curiosity about AI‑native analytics — NL2

SQL, semantic layers for self‑serve, and using tools like Claude and Genie to multiply your team's reach rather than replace rigor. Exposure to Unity Catalog, Looker/LookML, or reverse‑ETL/activation (Salesforce, Marketo, Gainsight) is a plus.

What we'll offer
Our compensation package reflects the impact you have on our mission. We benchmark to market value and conduct a yearly pay equity audit to keep pay fair and free of bias. For this role, the estimated base salary range is  167, CAD annually.  Actual base salary varies based on market and individual qualifications assessed during the interview process, and the range may be modified.

Your total package may also include equity and benefits; your Talent Acquisition Partner can walk you through the full picture.

We strive to provide equitable, competitive benefits that support our employees worldwide, which may include:

Mental health, wellness, and fitness benefits

Career coaching and support

Long‑term savings or retirement plans

In‑office culinary options to suit your dietary preferences

Benefits vary based on role, country, and local regulations.

About us
Asana is a leading platform for human + AI collaboration. Over 170,000 customers rely on Asana to align teams and accelerate organizational impact — bringing clarity to complexity and turning plans into action with AI working alongside teams at every step. We invest in our culture and our people because that's what fuels our growth and lets us achieve our mission.

Asana is an equal opportunity employer, and building a diverse, equitable, and inclusive company…