Senior Analytics Engineer

About the Data Team

The Data team sits within the Office of the COO and builds the models and frameworks that translate raw data into company-wide KPIs. We sit at the intersection of engineering and analytics, ensuring that data is transformed into the metrics Finance, Product, and Leadership need to operate — and to file for IPO with confidence.

What you’ll do

1. Design, build, and maintain reliable data models that transform raw data into business-ready datasets.
2. Collaborate closely with analysts, data scientists, and business stakeholders to understand requirements and translate them into actionable metrics, KPIs, and dashboards.
3. Develop and maintain metrics definitions, semantic layers, and data documentation to ensure consistency across teams.
4. Build, optimize, and test dbt models to deliver clean, reliable, and trusted data.
5. Ensure data quality, accuracy, and governance are embedded in all models and pipelines.
6. Create dashboards, reports, and visualizations that empower business users to make data-driven decisions.
7. Work with SQL and transformation frameworks to write efficient queries and maintain performant models.
8. Partner with data engineers to ensure smooth data ingestion and availability for analytics.
9. Continuously improve processes and workflows to increase efficiency, reliability, and scalability.

Would be great if you brought this to the role

1. Over 6 years of experience as a Analytics Engineer
2. Previous experience developing and tracking KPIs for public companies
3. Expert-level SQL skills with experience writing complex queries and optimizing performance.
4. Hands-on experience with dbt for data transformation and modeling.
5. Strong understanding of data modeling concepts (e.g., star schema, snowflake schema, dimensional modeling).
6. Familiarity with BI and dashboarding tools (e.g., Looker, Superset, Tableau, Power BI).
7. Experience defining KPIs and metrics for business stakeholders.
8. Comfort with Python or other scripting languages for lightweight data transformations and automation.
9. Knowledge of data governance, lineage, and documentation tools (e.g., DataHub, Great Expectations).
10. Understanding of cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
11. Collaborative mindset and ability to work closely with both technical and non-technical stakeholders.
12. Experience with version control and CI/CD practices in analytics workflows (e.g., GitHub Actions).

The salary range for US-based candidates only will be determined throughout the interview process depending on experience and skills.

US pay range (not including bonus, equity or other benefits)

$156,000—$187,000 USD