Sr. Data Engineer, Mircosoft Fabric

About the role

We are seeking a Senior Data Engineer with hands-on Microsoft Fabric experience to design, build, migrate, and support enterprise data platforms.

Responsibilities

• Design, develop, test, deploy, and maintain scalable end-to-end data pipelines for batch, incremental, and near-real-time processing.
• Build data ingestion and transformation solutions using Microsoft Fabric Data Factory, Data Pipelines, Dataflows Gen2, Notebooks, SQL, Python, PySpark, Spark, and T-SQL.
• Develop reusable, metadata-driven ETL/ELT frameworks that support relational databases, APIs, files, SaaS applications, cloud platforms, and structured or semi-structured data.
• Implement CDC, incremental loads, upsert/merge patterns, historical processing, error handling, monitoring, and data-quality controls.
• Lead legacy-to-modern data migrations, including data profiling, source-to-target mapping, cleansing, transformation, reconciliation, validation, and cutover support.
• Create enterprise data warehouse models using dimensional modeling, star schemas, fact tables, dimension tables, and slowly changing dimensions.
• Troubleshoot pipeline, data, integration, and performance issues and optimize Microsoft Fabric workloads and capacity utilization.
• Document data flows, mappings, standards, lineage, and operating procedures.
• Collaborate with technical and business stakeholders to deliver secure, governed, reliable, and scalable data solutions.

Requirements

• 5+ years of professional experience in data engineering, data integration, data warehousing, business intelligence, or a related field.
• Hands-on experience implementing Microsoft Fabric in a production or enterprise environment.
• Experience with Microsoft Fabric Data Factory, Data Pipelines, Dataflows Gen2, OneLake, Lakehouse, Fabric Warehouse, and Notebooks.
• Experience designing and implementing Medallion Architecture and enterprise ETL/ELT solutions.
• Strong SQL and T-SQL development skills.
• Strong Python and/or PySpark experience for data engineering and transformation.
• Experience with Spark-based batch and incremental data processing.
• Experience with legacy-to-modern data migration, data profiling, source-to-target mapping, transformation, reconciliation, and validation.
• Strong knowledge of data warehousing and dimensional modeling, including star schemas, fact tables, dimension tables, and slowly changing dimensions.
• Experience with data quality, monitoring, error handling, troubleshooting, and performance optimization.
• Strong analytical, problem-solving, written communication, verbal communication, and documentation skills.