Senior Manager Analytics Engineering

At Typeform, the Data & Insights team’s charter is to make data actionable through multiple mediums—reports, dashboards, AI/ML models, and robust data infrastructure—that help us fuel growth and drive efficiencies across the business. The Analytics Engineering function plays a foundational role in this mission: designing trusted, scalable data models that power strategic insights, experimentation, and automation across the company.

About the Role

We’re looking for a Manager or Senior Manager of Analytics Engineering—and we’re flexible on level for the right candidate. Whether you’re a seasoned senior manager or a strong manager ready to level up, we’d love to meet you.

As the Analytics Engineering leader at Typeform, you’ll lead a team of analytics engineers while also staying hands-on in the work. You’ll be responsible for developing our data modeling strategy, maturing our semantic and reverse ETL layers, and enabling cross-functional teams to confidently self-serve on business-critical metrics. You’ll play a key role in shaping the future of our analytics stack and will partner closely with stakeholders across R&D and GTM to ensure our data foundation scales with the business.

What You’ll Do

Strategic Leadership & Team Development (50-60%)

• Manage, mentor, and grow a team of 4–5 analytics engineers—fostering a culture of technical excellence, knowledge sharing, and continuous improvement.
• Define and prioritize the analytics engineering roadmap in partnership with stakeholders across Product, Engineering, Marketing, RevOps, and Data.
• Lead cross-functional efforts to align on core metrics, semantic layers, and taxonomy—ensuring scalable, reusable data assets across teams.
• Partner with Data Engineering to evolve our data platform and ensure pipelines are efficient, maintainable, and cost-optimized.
• Champion high standards for documentation, testing, and governance to ensure reliability and trust in our datasets.

Technical Execution & Hands-On Work (20-30%)

• Own the end-to-end development of canonical data models in dbt—ensuring clarity, performance, and alignment with business needs.
• Build and maintain key data pipelines and LookML models powering dashboards, experimentation, and ML workflows.
• Contribute to the development and monitoring of operational data flows (e.g. reverse ETL pipelines via Census).
• Implement and iterate on quality controls, including dbt tests, anomaly detection, and alerting tools (e.g. Monte Carlo, Great Expectations).
• Stay current on advancements in the modern data stack and continuously improve our tooling and development workflows.

Cross-Functional Enablement & Impact (20-30%)

• Translate business requirements into scalable, maintainable data solutions—enabling self-service and improving data access across the org.
• Advocate for data best practices and coach stakeholders on the effective use of our BI tools (Looker, etc.).
• Help define how success is measured through collaboration on experimentation instrumentation, analytics enablement, and metric standardization.
• Represent the Analytics Engineering function in planning cycles, vendor/tooling discussions, and cross-functional initiatives.

What You Bring

• 7+ years of experience in analytics/data engineering, with 2+ years in a team lead or management capacity.
• Deep expertise in SQL, dbt, and modeling performant data sets in modern cloud data warehouses (Snowflake, BigQuery, Redshift).
• Experience working with tools like Looker, Census, and workflow orchestrators (e.g. Airflow, Dagster).
• Familiarity with Python for scripting, automation, or orchestration tasks.
• Strong communication skills and a proven track record of cross-functional partnership and stakeholder alignment.
• A mindset of mentorship and a passion for helping others grow technically and professionally.

Extra awesome:

• You’ve led or contributed to initiatives around data governance, semantic modeling, or reverse ETL at scale.
• You’ve helped scale a data team or shaped processes for testing, CI/CD, and deployment of data models.
• Experience working with event tracking systems (e.g. Segment, Rudderstack) and supporting experimentation workflows (Amplitude, Growthbook).
• You’ve worked in a high-growth, product-led SaaS company and understand the importance of enabling fast, reliable decision-making.