Senior Manager Analytics Engineering

Summary

Join Typeform's Data & Insights team as a Manager or Senior Manager of Analytics Engineering. Lead a team of 4–5 analytics engineers, developing data modeling strategy, maturing semantic and reverse ETL layers, and enabling self-service on business-critical metrics. Partner with cross-functional teams (Product, Engineering, Marketing, RevOps, and Data) to align on core metrics and data assets. Develop canonical data models in dbt, build data pipelines and LookML models, and implement quality controls. Translate business requirements into scalable data solutions and advocate for data best practices. The ideal candidate has 7+ years of analytics/data engineering experience, including 2+ years in team lead or management. Deep expertise in SQL, dbt, and cloud data warehouses is required.

Requirements

• 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

Responsibilities

• 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
• 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
• 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

Preferred Qualifications

• 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

Benefits

• 5-10% bonus
• $170,000 — $200,000 USD