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