Data Engineer

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Warren MI - Onsite Role

Data Engineer – Role Summary & Job Description

(Legacy Support & Cloud Modernization – Azure Databricks)

Role Summary

We are seeking a Data Engineer to support and evolve our enterprise data platform, which integrates data from multiple ERP systems into a centralized analytics environment. This role is responsible for maintaining our existing SQL Server and SSIS-based data warehouse while driving the transition to a modern Azure-based architecture leveraging Azure Data Factory, Databricks (Lakehouse), and Power BI.

The position requires a balance of strong technical expertise and business acumen. The ideal candidate will not only build and maintain data pipelines, but also partner with business stakeholders to deliver high-quality, trusted data that drives decision-making, operational efficiency, and measurable business value.

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Key Responsibilities

Legacy Data Platform Support

• Maintain and enhance SSIS packages for data extraction, transformation, and loading

• Support SQL Server data warehouse (staging, ODS, reporting layers)

• Troubleshoot data issues, job failures, and performance bottlenecks

• Optimize SQL queries, stored procedures, and indexing strategies

• Ensure reliability of scheduled jobs via SQL Server Agent

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Cloud Data Engineering (Azure Databricks)

• Design and develop data pipelines using Azure Data Factory (ADF)

• Ingest and organize data into Azure Data Lake (Bronze/Silver/Gold layers)

• Build scalable data transformations using Databricks (Spark SQL, PySpark)

• Create curated, analytics-ready datasets for Power BI

• Implement Delta Lake and support data governance (e.g., Unity Catalog)

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Migration & Modernization

• Analyze and document existing SSIS/SQL pipelines

• Translate legacy ETL processes into modern ELT patterns

• Support phased migration strategy (coexistence of legacy and modern platforms)

• Reduce technical debt and improve pipeline maintainability

• Establish standards for data modeling, naming, and architecture

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Data Modeling & Business Value Creation

• Design dimensional models (fact and dimension tables) aligned to business processes

• Integrate and standardize data across multiple ERP systems

• Translate business requirements into scalable data solutions

• Partner with stakeholders to identify high-impact use cases for data and analytics

• Deliver datasets that enable reporting, forecasting, and operational insights

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Data Quality & Governance

• Implement data validation, reconciliation, and monitoring processes

• Ensure data accuracy and consistency across systems during migration

• Define and enforce data quality standards and controls

• Support data lineage, documentation, and transparency initiatives

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Collaboration & Stakeholder Engagement

• Work closely with business stakeholders, analysts, and BI developers

• Support Power BI semantic models and reporting solutions

• Communicate technical solutions in business terms

• Act as a bridge between IT/data teams and business functions

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Required Qualifications

• 4–8 years of experience in data engineering or data warehousing

• Strong SQL skills (T-SQL and/or Spark SQL)

• Hands-on experience with SSIS and SQL Server

• Experience with Azure Data Factory (ADF) or similar tools

• Experience with Databricks (Spark, Delta Lake, or similar platforms)

• Solid understanding of data warehousing concepts (star schema, fact/dimension modeling)

• Experience integrating data from multiple source systems (ERP experience preferred)

• Proven ability to translate business requirements into technical solutions

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Preferred Qualifications

• Experience migrating legacy ETL systems (SSIS) to cloud-based architectures

• Proficiency in Python or PySpark

• Familiarity with Medallion architecture (Bronze/Silver/Gold)

• Experience with Power BI data modeling and performance optimization

• Knowledge of data governance tools (e.g., Unity Catalog)

• Experience with Git and CI/CD pipelines

• Exposure to dbt or similar frameworks

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Technical Skills

• SQL Server (T-SQL), SSIS

• Azure Data Factory (ADF)

• Azure Data Lake Storage (ADLS)

• Databricks (Spark SQL, PySpark, Delta Lake)

• Data modeling (Kimball methodology preferred)

• Performance tuning and query optimization

• Version control (Git)