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Salary: USD 140,000 - 150,000 / annual
Clair is a fintech company on a mission to give America's workers financial freedom through on-demand pay. The company embeds its digital banking platform within scheduling, workforce management, and payroll apps that workers already use daily.
As an Analytics Engineer, you'll bridge engineering and analytics, building and maintaining Clair's data pipelines and data models. You'll work at the intersection of product, operations, and finance, partnering closely with the analytics team, data scientists, and business stakeholders to design cost-efficient, well-modeled data solutions that drive decision-making across the organization.
Key responsibilities include:
- Contributing to the ELT pipeline and applying best practices in modeling, testing, documentation, and observability
- Building and maintaining data models using dbt that power analytics and business decisions
- Ingesting and modeling data from new source systems into the data warehouse
- Partnering with analysts, engineers, and stakeholders to define metrics, data definitions, and governance standards
- Supporting data governance, access control, and monitoring across the data stack
- Identifying data quality issues and resolving root causes
- Optimizing Snowflake computing costs and maintaining code style guides
- Occasionally supporting the analytics team with ad-hoc or high-priority analysis
You'll need 3-4 years of professional data analytics experience, strong SQL proficiency, 1-2 years of hands-on dbt modeling in production, and familiarity with cloud data warehouses (Snowflake, BigQuery, Redshift) and data loading tools (Fivetran, Airbyte, Airflow). Understanding of data modeling concepts (dimensional modeling, Kimball) and experience with Python or R are required. You should be comfortable in a high-growth startup environment with strong communication skills to translate technical work into business value.
This is a hybrid role based in New York City with expectations to come into the office at least three days per week (Tuesdays, Wednesdays, Thursdays), with occasional additional days for client meetings.
