Lead Data Engineer (Business Financing)

Our client is a U.S.-based fintech company providing financing solutions to small and medium-sized businesses. Founded in 2006, the company operates as both a direct lender and a financing marketplace, having deployed more than $10 billion in growth capital to support over 65,000 businesses across the United States. Its technology- and data-driven solutions help business owners access funding quickly and efficiently.

The project focuses on transforming and scaling a modern data platform. The team is building a Snowflake-based data platform and converting existing Alteryx workflows into governed, production-ready data products. The initial phase is focused on Treasury and Finance, with additional business domains planned for future phases.

• 5+ years of experience as a Data Engineer
• Strong hands-on experience with Snowflake
• Advanced hands-on experience with dbt
• Experience with Dagster, Airflow, or similar orchestration platforms
• Strong background in modern data architecture and data engineering
• Experience with data quality, monitoring, and observability
• Ability to lead technical discussions and mentor engineers
• Experience collaborating across Engineering, BI, and Data Governance teams
• Comfortable using AI-assisted software development tools
• English proficiency at B2 level or higher
• Snowflake certification is preferred
• Availability to maximize overlap with U.S. Central Time working hours, especially from 2:00 p.m. CT onward, is a strong plus

• Provide technical leadership for a small team of Data Engineers
• Support implementation of the existing target data architecture
• Design and build scalable data pipelines and data products on Snowflake
• Develop and maintain dbt models and transformation workflows
• Use Dagster or Airflow for orchestration
• Review technical designs and support engineering decisions
• Collaborate with BI teams to understand and migrate existing Alteryx business logic
• Partner with Data Governance teams on data quality, definitions, and critical data elements
• Validate outputs, investigate data issues, and troubleshoot complex logic gaps
• Mentor engineers and establish strong engineering practices
• Use AI-assisted development tools such as Cursor and LLMs to improve engineering productivity