- Architect, build, and optimize data pipelines using Databricks, ADF, PySpark, and Unity Catalog.

- Implement data quality, lineage, privacy, and security governance across data assets.

- Integrate data from multiple sources to support analytics, ML feature stores, and enterprise insights.

- Operationalize ML models using MLflow and enhance productivity using LLM/Copilot solutions.

- Lead cross-functional data initiatives end-to-end; mentor engineers and enforce coding & platform standards.

- Lead the design and delivery of large-scale data engineering solutions on Azure and Databricks Lakehouse.

- This role focuses on scalable data pipelines, Unity Catalog governance, ML/LLM enablement, and driving engineering excellence.

- Drive efficiency through CI/CD, DevOps, automation, and reusable framework components.

Required Qualifications :

- 15+ years in Data Engineering with deep hands-on experience in Databricks Lakehouse, ADF, PySpark, Azure Data Lake, Unity Catalog.

- Strong background in ETL/ELT pipeline design, orchestration, and cloud data architecture.

- Experience with enterprise data governance, metadata, and lineage capabilities.

- Databricks Certification is mandatory (Data Engineer Associate/Professional).

- Azure Data Engineer / Cloud certifications preferred.