Senior Data Engineer - Data Vault & Snowflake Expert

pieinsurance; United States; On-site; USD 110,000 - 190,000; Full time

Job summary

Pie Insurance is hiring for a hands-on Data Engineer who will write production Python and SQL, build Airflow DAGs, and contribute to a Snowflake-based Data Vault 2.0 warehouse. You will develop data pipelines powering pricing, underwriting, and service analytics for small business insurance.

You will gain deep domain expertise in insurance data, work with cross-functional partners, and improve data quality, governance, and observability across the stack.

Qualifications

• Advanced proficiency writing complex SQL and manipulating large structured datasets.
• Proficiency in Python for building production-grade data pipelines.
• Hands-on Snowflake administration experience including RBAC, cost governance and access controls.
• Data Vault 2.0 experience strongly preferred; deep dimensional modeling is a plus.
• Experience designing and implementing modern data warehouses in cloud environments.

Responsibilities

• Develop complex data pipelines to transform raw data into reliable data models.
• Design, build, and maintain pipelines for accurate, trusted data with up-to-date freshness.
• Manage Snowflake warehouse sizing, performance, and security configurations.
• Build and maintain resilient Airflow DAGs and related CI/CD pipelines.
• Implement automated testing to catch issues in CI before production.
• Own production observability and respond to incidents with a focus on reliability and cost efficiency.

Skills

SQL

Python

Snowflake

Data Vault 2.0

Data modeling

Tools

Airflow

Job description

Pie Insurance is hiring for a hands-on Data Engineer who will write production Python and SQL, build Airflow DAGs, and contribute to a Snowflake-based Data Vault 2.0 warehouse. You will develop data pipelines powering pricing, underwriting, and service analytics for small business insurance.

You will gain deep domain expertise in insurance data, work with cross-functional partners, and improve data quality, governance, and observability across the stack.