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Salary: USD 170,000 - 220,000 / annual
Coast is reimagining the trillion-dollar U.S. B2B card payments infrastructure, focusing on the country's 500,000 commercial fleets and 40 million commercial vehicles. The company is building modern digital experiences and transparent financial services products to replace decades-old incumbent technologies.
You will lead a small, high-leverage data engineering team and serve as a thought leader for the company's data strategy. Data sits at the center of Coast's operations, and your role is to make it not just available but accessible to everyone in the company. You'll work within a federated data model where product teams own their data products, and your platform team provides patterns, tools, and guardrails.
Key responsibilities include:
**Own the Data Warehouse**: Set standards and build patterns for data ingestion. Enable data consumers to build models efficiently. Build new capabilities like data catalogs and semantic layers that make data discoverable across the company. Establish data security standards including tokenization, RBAC, and row-level security.
**Provide High-Quality Patterns**: Develop opinions and expertise on the best ways to produce and consume data. Figure out how to make data usable via reverse ETL or AI agents. Help the organization evolve from treating data as exhaust to building domain-driven data products.
**Be a Teacher**: Help product engineering teams adopt modern data practices. Teach non-technical users how to consume data using AI tools without SQL. Evangelize new practices and patterns.
**Write Code**: Contribute to data pipelines in Python. Build CI/CD pipelines for data products. Contribute new data tests for dbt.
The company already runs on Redshift, Dagster, and dbt. Major pieces you'll build include a data catalog, semantic layer, mature security posture, and AI-ready data access.
Success in your first year means: providing a model for understanding data warehouse costs, shipping a data catalog, establishing a data security strategy with real progress on row-level security and RBAC, and creating a strategy for safely making data accessible to AI agents.
**Requirements:**
- 7+ years hands-on experience across the data ecosystem (ETL/ELT, orchestration, data warehouses, columnar stores, BI tooling, SQL optimization)
- AI-native mindset; already using AI tools daily
- Experience with Redshift or Snowflake, Dagster, dbt, and medallion architecture
- Proficiency in Python
- Experience leading a horizontal Data Engineering team, working with analytics and product engineering teams
- Mentorship experience with data engineers and other team members working with data products
- Excellent written and verbal communication
- Bias toward action and process-building
- Comfortable in ambiguity; can build structure without losing momentum
- NYC-based; able to be in-office 3–4 days per week
