Senior Manager of Analytics Engineering, Typeform

• 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
• 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

Desirable

• 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
• 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
• 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