Job Description:

Job Description: Data Engineer – Databricks & Microsoft Fabric

Location : Chennai, India
Experience : 6 to 8 years

Role Summary
We are looking for an experienced Data Engineer with strong hands-on expertise in Databricks, Microsoft Fabric, PySpark, SQL, and cloud-based data engineering. The candidate will be responsible for designing, developing, and optimizing scalable data pipelines, lakehouse solutions, and analytics-ready data models.
The role requires solid experience in building end-to-end data engineering solutions, working with structured and semi-structured data, implementing data quality controls, and supporting enterprise reporting and analytics platforms.
Key Responsibilities
Data Engineering and Pipeline Development
• Design, build, and maintain scalable data pipelines using Databricks, PySpark, Spark SQL, and Microsoft Fabric.
• Develop batch and incremental data ingestion pipelines from multiple source systems.
• Build and optimize Bronze, Silver, and Gold layer data models using lakehouse architecture.




• Implement ELT/ETL workflows for data transformation, enrichment, validation, and publishing.
• Work with structured, semi-structured, and unstructured data formats such as CSV, Parquet, JSON, Delta, and XML.
Databricks Development
• Develop notebooks, jobs, workflows, and reusable components in Azure Databricks.
• Implement Delta Lake features such as schema evolution, merge/upsert, time travel, and optimized storage.
• Optimize Spark jobs for performance, scalability, and cost efficiency.
• Implement partitioning, caching, indexing, and cluster optimization strategies.
• Troubleshoot job failures, performance bottlenecks, and data quality issues.
Microsoft Fabric Development
• Build data solutions using Microsoft Fabric Lakehouse, Warehouse, Data Factory, Pipelines, Notebooks, and OneLake.
• Develop and manage data pipelines in Fabric for ingestion, transformation, and orchestration.
• Work with Fabric SQL endpoints, semantic mo

📌 Data Engineer (Chennai)
🏢 NTT Data
📍 Chennai