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Data Engineer

Job in Saint Peters, St. Charles County, Missouri, 63376, USA
Listing for: CarShield
Full Time position
Listed on 2026-08-28
Job specializations:
  • IT/Tech
    Data Engineering, Data Warehousing
Salary/Wage Range or Industry Benchmark: 130000 - 175000 USD Yearly USD 130000.00 175000.00 YEAR
Job Description & How to Apply Below

Car Shield is seeking a Data Engineer to build and maintain data pipelines that power analytics and decision-making across our finance and insurance operations. You will design and optimize ETL/ELT workflows from internal systems and third-party sources, develop and manage data warehouses/lakes, and ensure data quality, reliability, and security. Partner with analysts, data scientists, and business stakeholders to model data that supports pricing, risk, claims, and customer insights.

This role suits someone who thrives in a fast-paced, customer-focused environment and enjoys solving complex data challenges to drive measurable business impact.

Responsibilities

  • Design, build, and maintain scalable ETL/ELT data pipelines from multiple internal and external sources.
  • Develop and manage data warehouses/lakes to support analytics, reporting, and data science use cases.
  • Implement and monitor data quality, validation, and reliability checks across pipelines and datasets.
  • Collaborate with analysts and business teams to design data models that support pricing, risk, claims, and customer insights.
  • Optimize SQL and data storage for performance, cost, and reliability in cloud environments.
  • Ensure data security, access control, and compliance with relevant policies and regulations.
  • Document data flows, schemas, and processes to support maintainability and knowledge sharing.
  • Troubleshoot and resolve data pipeline incidents in a fast-paced, production environment.

Required Skills

  • SQLPython
  • ETL/ELT pipeline development
  • Data warehousing
  • Cloud platforms (AWS/Azure/GCP)
  • Data modeling (star/snowflake)
  • Version control (Git)
  • Relational databases (Postgre
  • SQL/My
  • SQL/SQL Server)
  • Data quality and validation
  • BI/analytics tools (e.g., Power BI, Tableau)
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