Data Engineer

Data Engineer

Location:

South Bend, IN (Hybrid)
Salary:
Up to $125,000 (Mid-Level) | Up to $150,000 (Senior-Level)

An organization in South Bend, IN is seeking a Data Engineer to play a critical role in building and optimizing a centralized enterprise data platform. This position is ideal for a hands-on engineer who thrives in modern Azure environments and has strong experience designing scalable data pipelines using Databricks, Python, and Spark.

As the organization consolidates data into a single, unified platform, this role will focus on engineering robust ETL/ELT processes, modernizing legacy data workflows, and ensuring reliable, high-performance data delivery across the enterprise.

This is a highly visible role working closely with analytics, IT, and business stakeholders to support enterprise-wide reporting, operational intelligence, and long-term data strategy.

• Strong experience with Azure data services (Azure Data Factory, Azure SQL, Synapse, etc.)

• Hands-on experience with Databricks

• Strong proficiency in Python and Spark

• Experience developing and optimizing ETL/ELT pipelines

• Background working with legacy Microsoft tools such as SSIS and SSRS

• Deep understanding of data modeling, transformation logic, and pipeline architecture

• Experience consolidating data into centralized or cloud-based platforms

• Strong SQL skills

• Ability to troubleshoot performance issues and optimize large-scale datasets

• Strong communication skills and ability to collaborate cross-functionally

• Experience supporting enterprise BI/reporting environments

• Exposure to ERP, operational, or transactional data systems

• Experience in manufacturing, agriculture, supply chain, or similar industries

• Familiarity with data governance and best practices

• Design, build, and maintain scalable data pipelines using Databricks, Azure, Python, and Spark

• Migrate and modernize legacy SSIS/SSRS workflows into the centralized data platform

• Develop robust ETL/ELT processes to integrate data from multiple enterprise systems

• Support the design of a unified enterprise data model

• Optimize data processing performance and reliability

• Ensure data quality, validation, and integrity across pipelines

• Partner with BI, analytics, and business teams to support enterprise reporting needs

• Contribute to data standards, governance, and architectural best practices

• Support the organization’s transition to a modern, centralized data ecosystem