Data Engineer

Role overview

Hands-on Data Engineer role supporting Pie’s data infrastructure for commercial insurance. You’ll build production data pipelines and contribute to a Data Vault 2.0 warehouse powering core workflows like quoting, underwriting, and servicing.

Responsibilities

• Design, build, and maintain reliable data pipelines that provide accurate and timely data.
• Make data modeling decisions in Data Vault 2.0, balancing raw fidelity with downstream consumption.
• Administer and optimize Snowflake, including:
  • warehouse sizing, query performance
  • access controls / RBAC and user/role management
  • cost tuning
• Build resilient Airflow DAGs and CI/CD pipelines with predictable, safe rollbacks.
• Implement automated testing for unit, integration, and data quality so issues are caught in CI.
• Provide production support with proactive observability, incident response, and feedback loops into validation and CI/CD.
• Use AI-powered coding tools (e.g., Claude Code, Cursor, Snowflake Cortex) to speed development and improve code quality.
• Partner with stakeholders (Executive, Product, Engineering, business teams) to translate needs into technical solutions.
• Run technical projects end-to-end (scoping, requirements documentation, and trade-off explanations) with guidance from senior engineers.
• Follow data governance/privacy/security best practices and support SOX-relevant change management and validation.

Requirements

• 2+ years experience as a software engineer or data engineer focused on data systems.
• Advanced ability writing complex SQL and working with large structured/semi-structured datasets.
• Python proficiency for production-grade data pipelines.
• Working knowledge of Snowflake (or comparable cloud warehouse), including performance and access controls.
• Familiarity with cloud warehouse modeling; experience with Data Vault 2.0 is required (willingness to learn if needed).
• Experience with testing frameworks for data/code validation in production.
• Hands-on experience with data observability tooling.
• Proficiency using AI coding assistants as part of the development workflow.

Nice to have

• Exposure to RBAC and cost governance.
• Experience with Redshift or BigQuery.
• Familiarity with dimensional modeling or Data Vault 2.0.