This is a hands-on engineering role — you'll be writing production Python and SQL, building Airflow DAGs, and contributing to our Data Vault 2.0 warehouse alongside a team of staff engineers.

You'll work on the data infrastructure that powers how Pie quotes, underwrites, and services small business insurance customers. The pipelines and models you build feed everything from pricing to financial reporting, which means correctness and reliability matter.

You'll be expected to develop deep domain expertise in insurance and the Pie business over time. Engineers who succeed here understand premium, loss, and policy lifecycle as well as they understand Snowflake.

How You’ll Do It

• Develop complex and efficient data pipelines to transform raw data sources into reliable, well-tested components of our data models.
• Design, build, and maintain data pipelines that deliver accurate, trusted data with the freshness our stakeholders depend on.
• Make data modeling decisions within our Data Vault 2.0 warehouse that balance raw fidelity in the vault with the consumption patterns of downstream marts and analytics.
• Administer and optimize Snowflake across warehouse sizing, query performance, access controls, RBAC and user/role management, and ongoing cost tuning.
• Build and maintain resilient Airflow DAGs and CI/CD pipelines that make deployments predictable, repeatable, and safe to roll back.
• Implement automated testing (unit, integration, and data quality) so issues are caught in CI before they reach production.
• Own production observability through our internal tooling, tuning alerts, responding to incidents, and closing the loop from production issues back into pre-load validation and CI checks.
• Leverage AI-powered tools (e.g., Claude Code, Cursor, Snowflake Cortex) as a core part of your development workflow to accelerate code generation, automate documentation, and improve code quality.
• Work with stakeholders across Executive, Product, Engineering, and business teams to translate the "why" behind a request into a technical solution that meets the business need.
• Lead technical projects end-to-end, scoping with stakeholders, documenting requirements, and explaining technical trade-offs without relying on a Product Manager.
• Drive cross-team initiatives that require influence and alignment to achieve a common goal.
• Drive best practices for data governance, privacy, and security, including the change management and validation discipline required for SOX-relevant reporting.
• Take an active part in the operational responsibilities of running our data infrastructure, with a focus on reliability, cost efficiency, and observability.

The Right Stuff

• Minimum 5 years experience as a software engineer or data engineer with a focus on data systems.
• Advanced proficiency writing complex SQL and manipulating large structured and semi-structured datasets.
• Proficiency in Python for building production-grade data pipelines.
• Hands-on Snowflake administration experience, including warehouse management, RBAC and role design, access controls, and cost governance.
• Demonstrable experience designing and implementing modern data warehouses, with an understanding of best practices.
• Experience modeling data in cloud data warehouses such as Snowflake, Redshift, or BigQuery. Data Vault 2.0 experience strongly preferred, deep dimensional modeling experience acceptable with willingness to learn Data Vault.
• Demonstrated experience using testing frameworks to validate data and code in a production environment.
• Hands-on experience with data observability tooling.
• Proficiency using AI coding assistants (e.g., Claude Code, Cursor, Snowflake Cortex) as a core part of your development workflow.
• Track record of leading technical projects end-to-end without a Product Manager — from scoping through documentation through stakeholder communication.
• Comfort working in a regulated environment (e.g., SOX-relevant financial reporting).
• Willingness to develop deep domain expertise in insurance and the Pie business over time. The best data engineers here understand premium, loss, and policy lifecycle as well as they understand Snowflake.

The use of AI in Application Review: To support a fair, efficient, and consistent hiring process, we use AI-powered tools to assist in the initial screening of applications. These tools help us identify qualifications and prior work experiences that align with the requirements of the role. We may also use AI assistant video tools during interviews to support note-taking and candidate evaluation. All AI-powered outputs are still subject to human oversight and decision-making at multiple stages of the process. By submitting your application, you acknowledge and consent to Pie utilizing these AI technologies to assist in our evaluation process.