MS Fabric Data Engineer

Ovation Healthcare•Crest, CA

1d•Remote

About The Position

As a Microsoft Fabric Data Engineer, you will design, build, and maintain scalable data pipelines, data models, and analytics solutions using Microsoft Fabric and Azure Data Services. You will work extensively within Fabric's Lakehouse and Warehouse environments — building well-structured data models and developing Fabric Notebooks (PySpark/Spark SQL) for data transformation at scale — and collaborate with cross-functional teams to deliver high-quality, secure, and efficient data solutions that drive business intelligence and advanced analytics initiatives.

Requirements

Demonstrated expertise in Microsoft Fabric data modeling and engineering, including Lakehouse vs. Warehouse architecture, OneLake, Delta Lake tables, medallion architecture, and designing star-schema semantic models for Direct Lake or Import mode reporting.
Hands-on proficiency writing and maintaining Fabric Notebooks using PySpark, Spark SQL, and/or Python for large-scale data transformation, including Spark performance tuning (partitioning, caching, cluster sizing).
Proficiency with the broader Microsoft Fabric and Azure Data Services ecosystem (e.g., Data Factory/Fabric Pipelines, Dataflows Gen2, Azure Synapse Analytics, Azure Data Lake).
Strong understanding of data engineering concepts including ETL/ELT processes, data pipeline architecture, and data integration.
Experience with SQL and programming languages such as Python or Scala for data manipulation.
Familiarity with cloud computing platforms and best practices for scalability and security.
Knowledge of data governance, data quality, and compliance standards.
Ability to work with business intelligence tools (e.g., Power BI) and understand analytics requirements, including how well-designed data models drive report performance.
Strong problem-solving skills and the ability to optimize performance in large data environments.
Excellent communication and collaboration skills for working with cross-functional teams.
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
3+ years of experience in data engineering, data architecture, or a similar role.
Proven, hands-on experience building data models and Notebooks within Microsoft Fabric (Lakehouse and/or Warehouse), not just Power BI report-building.
Demonstrated experience writing PySpark/Spark SQL in a notebook environment for data transformation and pipeline development.
Strong programming skills in SQL and at least one language such as Python, Scala, or similar.
Experience designing and implementing complex data pipelines and ETL/ELT workflows.
Familiarity with cloud infrastructure, security best practices, and compliance standards.
Excellent analytical and problem-solving abilities.
Strong communication skills with the ability to collaborate effectively across technical and business teams.

Nice To Haves

Microsoft certifications such as Fabric Data Engineer Associate (DP-700), Azure Data Engineer Associate, or Microsoft Certified: Data Analyst Associate are a plus.

Responsibilities

Design, develop, and maintain scalable, reliable, and efficient data pipelines using Microsoft Fabric and Azure Data Services.
Design and implement data models within Microsoft Fabric — including Lakehouse and Warehouse architectures, medallion (bronze/silver/gold) layering, and star-schema semantic models optimized for Direct Lake reporting.
Build, maintain, and optimize Fabric Notebooks (PySpark, Spark SQL, and/or Python) to ingest, cleanse, and transform data at scale, applying performance best practices for Spark compute and Delta Lake tables.
Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver actionable insights.
Implement data ingestion, transformation, and integration workflows (pipelines, Dataflows Gen2, and notebooks) following best practices for security and performance.
Evaluate and choose the right Fabric tool for the job — pipelines, Dataflows Gen2, or notebooks — based on complexity, performance, and maintainability tradeoffs.
Monitor, troubleshoot, and optimize data pipelines, notebooks, and data models to ensure high availability and accuracy.
Ensure data solutions comply with organizational data governance policies and industry regulations.
Document data models, architectures, processes, and workflows for operational transparency and knowledge sharing.
Stay current with Microsoft Fabric and Azure ecosystem updates and recommend improvements or new technologies.
Support the deployment and maintenance of business intelligence and reporting platforms, including Power BI semantic models built on Fabric data.