Data Engineer

Pie Insurance

14d•$115,000 - $145,000•Remote

About The Position

Pie's mission is to empower small businesses to thrive by making commercial insurance affordable and as easy as pie. We leverage technology to transform how small businesses buy and experience commercial insurance. Like our small business customers, we are a diverse team of builders, dreamers, and entrepreneurs who are driven by core values and operating principles that guide every decision we make. 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 senior and 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.

Requirements

Minimum 2+ 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.
Working knowledge of Snowflake or comparable cloud warehouse — comfortable with warehouse management, access controls, and query performance. Exposure to RBAC and cost governance is a plus.
Experience working with data models in cloud data warehouses such as Snowflake, Redshift, or BigQuery. Familiarity with Data Vault 2.0 or dimensional modeling is a plus, willingness to learn Data Vault is required.
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.
Experience running technical projects end-to-end with guidance from senior engineers — scoping, documenting, and communicating with stakeholders.
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.

Nice To Haves

Exposure to RBAC and cost governance is a plus.
Familiarity with Data Vault 2.0 or dimensional modeling is a plus, willingness to learn Data Vault is required.

Responsibilities

Design, build, and maintain data pipelines that deliver accurate, trusted data with the freshness our stakeholders depend on.
Contribute to 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.
Provide mission-critical production support and ensure data reliability through proactive observability tuning, comprehensive incident response, and continuous feedback loops that integrate production insights back into pre-load validation and CI/CD pipelines.
Leverage AI-powered tools (e.g., Claude Code, Cursor, Snowflake Cortex) 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.
Run technical projects end-to-end with guidance from senior engineers — scoping with stakeholders, documenting requirements, and explaining technical trade-offs.
Follow and contribute to 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.