Manager of Analytics Engineering

Typeform

4d•$170,000 - $200,000•Remote

About The Position

We’re looking for a Manager of Analytics Engineering. 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.

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.

Nice To Haves

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.

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.