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# Data Engineer

- **Company:** Allstate Corporation
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $128,000.0 - $168,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Systems, Data Warehousing, Document-Oriented Databases, Cloud Services, Snowflake, Git, Real Time Data, Software Version Control, Data Pipelines
- **Published:** October 16, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=3023584956-2

## About the Role

* Data engineering
 * ETL/ELT development
 * SQLPython or Scala
 * Cloud data platforms (AWS, Azure, or GCP)
 * Data warehousing (e.g., Snowflake, Redshift, Big
 * Query)
 * Data modeling
 * Data pipeline orchestration (e.g., Airflow)
 * Version control (e.g., Git)
 * Data quality and governance

## Description

Allstate is seeking a Senior Data Engineer to build and optimize data pipelines that power analytics and decisioning across our finance and insurance products. In this role, you will design and maintain scalable data architectures on cloud platforms, develop robust ETL/ELT workflows, and ensure data quality, security, and reliability. You'll partner with data scientists, analysts, and product teams to deliver curated datasets and real-time data solutions, improve performance, and implement best practices. This position offers growth, learning opportunities, and impact in a collaborative, flexible environment., * Design, build, and maintain scalable cloud-based data pipelines and architectures

 * Develop and manage ETL/ELT processes to integrate diverse data sources
 * Ensure data quality, governance, security, and reliability across platforms
 * Optimize data models and queries for analytics, BI, and data science workloads
 * Collaborate with data scientists, analysts, and product teams on data solutions
 * Implement best practices for version control, testing, and CI/CD for data workflows
 * Monitor, troubleshoot, and improve performance of data pipelines and jobs
 * Document data architectures, standards, and processes for stakeholders