Data Engineer (Microsoft Fabric), Mesa, AZ

Kforce's client, a growing technology company in the Mesa/Phoenix, AZ area, is seeking a Mid-Level to Senior Data Engineer with strong Microsoft Fabric expertise. This is a direct hire opportunity working closely with the hiring manager. The company offers a competitive compensation package and a hybrid work model (must reside in the Phoenix metro area). Position Overview: This role focuses on designing, building, and optimizing scalable data pipelines and platform components within Microsoft Fabric. The Data Engineer will play a key role in enabling analytics and machine learning initiatives, ensuring data is high-quality, governed, and performant while collaborating across IT and business teams. Responsibilities:

• Design and manage Fabric Lakehouse architectures (OneLake, medallion patterns)
• Build and orchestrate ETL/ELT pipelines using Data Factory, Spark, and SQL
• Optimize and administer Fabric workloads, capacity, and performance
• Support delivery of ML-ready datasets, feature stores, and inference pipelines
• Implement CI/CD pipelines and support model deployment lifecycle (MLOps)
• Establish data quality, lineage, governance, and monitoring standards
• Optimize Spark/SQL performance for scalability and cost efficiency
• Collaborate cross-functionally and contribute to best practices and reusable assets
• Bachelor's or Masters degree in Computer Science, Information Systems, Data Engineering or related field
• At least 3-5+ years of Data Engineering experience
• Strong hands-on experience with Python and SQL
• Proven experience with Microsoft Fabric (Lakehouse, OneLake, Data Factory, Spark) and Power BI integration
• Experience with modern data formats (Delta Lake, Parquet, Spark)
• Familiarity with data governance (Purview, RBAC)
• Experience with CI/CD, Git, and automated testing for data platforms

• Experience integrating Fabric with Azure services (ADLS, Azure SQL/MI, Event Hubs, Synapse)
• Exposure to MLOps practices (feature stores, model registry, monitoring)
• Knowledge of Power BI semantic models, Direct Lake/DirectQuery
• Experience with streaming or near real-time data architectures