Senior Data Engineer with strong Microsoft Fabric

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 Preferred Qualifications 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 #J-18808-Ljbffr