Microsoft Fabric Data Engineer

Lakehouse Development

• Design and maintain Fabric Lakehouse environments.
• Develop and optimize Delta Tables for high-performance analytics workloads.
• Implement Medallion Architecture (Bronze, Silver, Gold layers) to support data quality and governance requirements.
• Create curated datasets for reporting, analytics, and AI/ML use cases.

Data Transformation & Processing

• Develop data transformation logic using PySpark, Spark SQL, and notebooks.
• Create reusable frameworks and data engineering best practices.
• Perform data cleansing, enrichment, and standardization activities.
• Data Modeling & Analytics Support
• Design dimensional and analytical data models.
• Collaborate with Business Intelligence teams to support Power BI semantic models and reporting requirements.
• Ensure data consistency, quality, and accuracy across data platforms.

Graduate in Computer Science, Data Science, or related field. 4-6 years of experience in data engineering or related field.

Required Skills

Technical Skills

• Microsoft Fabric
• OneLake
• Fabric Lakehouse
• Fabric Data Factory
• Data Pipelines
• Notebook Development
• PySpark
• Spark SQL
• SQL
• Delta Lake
• Medallion Architecture
• Power BI Integration
• Git Version Control

Cloud & Data Platform Experience

• Azure Data Services
• Azure Data Lake Storage (ADLS)
• Azure Synapse Analytics (preferred)
• Azure DevOps

Data Engineering Concepts

• ETL/ELT Development
• Data Modeling
• Data Warehousing
• Data Governance
• Data Quality Frameworks
• Performance Tuning

Preferred Qualifications

• Experience in large-scale enterprise data transformation programs.
• Knowledge of Data Mesh and Modern Data Platform concepts.
• Exposure to AI/ML data preparation processes.
• Microsoft Fabric Certification (Preferred).

Experience working in Banking, Insurance, Healthcare, or Retail domains.

Key Responsibilities

Data Engineering & Integration

• Design, develop, and maintain scalable data ingestion and transformation pipelines using Microsoft Fabric.
• Build and manage ETL/ELT workflows using Fabric Data Factory and Data Pipelines.
• Integrate data from multiple sources including databases, APIs, flat files, cloud storage, and SaaS applications.
• Implement incremental data loads and Change Data Capture (CDC) strategies.