Job Summary

Designed and implemented a scalable enterprise Lakehouse platform on Azure using Azure Data Factory, Delta Lake, ADLS Gen2, and Databricks. Migrated legacy ETL processes to cloud-native ELT pipelines supporting both batch and near real-time workloads. Established reusable data frameworks, metadata-driven orchestration, data quality validation, and automated CI/CD pipelines.

Responsibilities

- Designed and implemented scalable data pipelines using Azure Data Factory, Databricks, Delta Lake, Python, and SQL.
- Architected Lakehouse solutions supporting enterprise analytics, AI/ML, and Generative AI workloads.
- Developed structured and unstructured data pipelines for RAG, vector search, and AI-powered applications.
- Built reusable frameworks, improving code quality, scalability, and development productivity.
- Optimized ETL/ELT processes, reducing data latency and improving performance.
- Implemented CI/CD, automated testing, monitoring,



and DataOps best practices.
- Established data quality, governance, security, and metadata management standards.
- Collaborated with cross-functional teams to deliver business-driven data and AI solutions.
- Mentored engineers and led technical design and architecture discussions.
- Drove cloud modernization initiatives, improving reliability, scalability, and operational efficiency.

Skills

Must have

- 5+ years of experience
- Azure Data Factory (ADF)
- Databricks
- Delta Lake
- Python
- SQL / pl-sql
- PySpark
- Azure Data Lake (ADLS)
- CI/CD (Azure DevOps)
- Data Modeling
- ETL/ELT Pipelines

Nice to have

- Azure AI Search
- Microsoft Fabric
- Kafka
- Snowflake
- Terraform
- Docker / Kubernetes
- Microsoft Purview

Location - pune,mumbai,chennai,banagalore

📌 Azure AI Data Engineer (Mumbai)
🏢 Luxoft
📍 Mumbai