Senior Data Engineer - Human Interest

About the role

As a Senior Data Engineer on the Data Access team, you will be a technical anchor for Human Interest’s data platform at a pivotal moment in our growth. This role exists to build and own the infrastructure that ensures the data powering our products, customer reporting, and business metrics are reliable and well governed. You will drive the evolution of our data platform toward an architecture that scales for AI consumption and for Human Interest's next growth stage.

The Data Access team is Human Interest's internal data platform team. We own the full data stack from ingestion and orchestration through transformation, governance, and delivery to BI tools. Our stack includes Snowflake, dbt, Airflow, Meltano, and Terraform on AWS.

What you get to do every day

• 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 AWS, Terraform, Airflow, Snowflake, dbt, Meltano, and 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.

What you bring to the role

• 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.

Nice to haves

• 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.