Analytics Engineer

Build, maintain, and optimize analytics data stack, including ELT pipelines, dimensional models, and semantic layers using dbt, Snowflake, and Airflow

Collaborate with Marketing, Product, Operations, and Finance teams to define key metrics, improve reporting fidelity, and enable self-serve analytics

Own and evolve core data models and pipelines that drive dashboards, reporting, and downstream analysis

Apply best practices for data testing, observability, and CI/CD to ensure reliable, version-controlled, production-ready analytics code

Partner with stakeholders to understand business needs and translate them into performant, trustworthy data products

Contribute to internal data documentation and governance practices

Mentor team members on data modeling, tooling, and processes, bringing a scalable, engineering-first approach to analytics

4–6 years of experience in analytics engineering, data analytics, or data engineering, ideally in fast-paced or high-growth environments

Strong SQL expertise and experience with large-scale, normalized, or dimensional datasets

Hands-on experience building data models with dbt in a production environment (CI, testing, documentation)

Experience with cloud data warehouses like Snowflake, BigQuery, or Redshift

Experience building semantic layers and enabling self-serve BI (Looker, Sigma, Tableau)

Proactive communicator able to collaborate with business stakeholders and technical teams

Strong ownership and accountability for data quality, performance, and maintainability

Familiarity with Python and orchestration tools (Airflow, Prefect, Dagster) for data pipelines is a plus

Autonomy & ownership – create your own path and own your work

Competitive compensation and equity incentives

Generous time off program

Health & dental benefits