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# Senior Data Engineer (Azure Databricks)

- **Company:** Finoit Inc
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $148,000.0 - $164,000.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Cloud Engineering, Data as a Services, Data Architecture, Information Engineering, Data Systems, Software Debugging, Python (Programming Language), Metadata, Performance Tuning, Cloud Services, SQL Databases, Data Processing, Data Ingestion, Azure Data Factory, Software Troubleshooting, Information Technology, Data Pipelines, Databricks
- **Published:** September 18, 2026
- **Apply:** https://www.dice.com/job-detail/30ae3ea1-8f79-4b4a-b629-eccff5323d6f

## About the Role

* Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
 * 6+ years of hands-on experience building and owning end-to-end data pipelines.
 * Expert-level proficiency in Python and SQL.
 * Strong experience with data ingestion, transformation, validation, and governance.
 * Ability to design scalable data architectures and clearly communicate technical solutions.
 * Experience building reusable, metadata-driven pipelines.
 * Strong troubleshooting, debugging, and performance optimization skills.
 * Hands-on experience with data modeling, testing, monitoring, and data quality practices.

Preferred Qualifications

 * Strong experience with Azure Databricks (required/preferred platform).
 * Experience with Azure Data Factory and other Azure cloud data services.
 * Familiarity with enterprise data engineering best practices and cloud-native architectures.

## Description

We are seeking a highly skilled Senior Data Engineer to join a growing data engineering team. The ideal candidate will have strong experience designing, building, and maintaining scalable end-to-end data pipelines in modern cloud environments, with expertise in Azure Databricks., * Design, develop, and maintain scalable end-to-end data pipelines.

 * Build data ingestion, validation, transformation, and governance frameworks.
 * Develop reusable, metadata-driven data pipeline solutions.
 * Optimize data processing performance and troubleshoot complex data issues.
 * Design and implement scalable data architecture to support business needs.
 * Collaborate with cross-functional teams to deliver reliable data solutions.
 * Implement data quality, monitoring, and testing best practices.
 * Work with cloud-native data platforms and services to support enterprise analytics.


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