Senior Data Engineer

Human Interest is a high-growth fintech company dedicated to providing retirement benefits to all workers. As a Senior Data Engineer, you will build and own the infrastructure for the data platform, ensuring reliable data for products and reporting while driving the evolution towards AI consumption.

Responsibilities:

• Define and maintain data governance standards including access controls, data lineage, and contracts between data producers and consumers
• Lead the technical direction and evolution of our data platform as we move toward an AI-first data infrastructure. You will design for AI consumption, unstructured data access, and integration with AI tooling from the ground up
• Build and scale data pipelines and architectures that make data accessible and useful to both human analysts and AI systems
• Own end-to-end design and development of scalable data pipelines, from ingestion and orchestration to transformation and delivery, using tools including AWS, Terraform, Airflow, Snowflake, dbt, Meltano, Python
• Drive data platform reliability through performance optimization, data quality monitoring, and SLA-based prioritization of our most critical data assets
• Leverage and champion AI-assisted development tools, including Claude Code, to accelerate development velocity across the team
• Mentor data engineers and analysts, raising the technical bar across the team

Requirements:

• 5+ years of experience as a Data Engineer with a strong focus on production data pipelines and data infrastructure development
• Strong experience with AWS data services in a production context, including storage, compute, and pipeline tooling
• Hands-on experience with managing cloud data warehouse technologies, including Snowflake or equivalent, covering access control, performance tuning, and cost management
• Strong Python skills, with the ability to independently own and improve complex production data pipelines
• Experience with workflow orchestration at scale, including Airflow or equivalent tools
• Familiarity with data governance, observability, and data quality practices
• Familiarity with event sourcing or change data capture-based data patterns
• A strong desire to leverage AI tools and workflow automation to improve team productivity
• Experience with data lakehouse architectures and open table formats such as Apache Iceberg or Delta Lake
• Experience with dbt-core in a production repository
• Experience building data infrastructure to support AI-driven data access such as exposing data assets via MCP, implementing governance frameworks for AI data access, and curating, monitoring and evaluating the quality of AI-generated query responses
• Background in fintech, financial services, or another highly regulated or compliance-driven industry