Role & responsibilities:

9-12 years of overall experience in IT, Data Engineering, and a minimum of 5 years of hands-on experience with Databricks in production environments, in cloud-based data engineering, with a strong background in designing, implementing, and supporting modern data lakehouse architectures.

- Design and build Databricks Lakehouse architecture specific to the client's context: Unity Catalog hierarchy, cluster policies, network configuration (VNET injection or Private Link), and medallion layer design justified against their security, compliance, and budget constraints
- Implement Databricks Unity Catalog governance: three-level namespace design (metastore/catalogue/schema), column-level lineage, agile data masking, row-level security, and Delta Sharing for cross-cloud data access
- Build AI-ready data pipelines: embedding generation workflows, document chunking and indexing pipelines, and semantic search indices using Databricks Vector Search
- Implement end-to-end monitoring:



Databricks job failure alerts, data freshness SLA checks, row count anomaly detection, data quality expectation failures via DLT

Preferred candidate profile Databricks Lakehouse Platform Delta Lake, Unity Catalog, Delta Live Tables, Databricks Workflows, Databricks SQL, Multi-Cloud Data Engineering — Azure (Synapse, ADF, ADLS Gen2) or AWS (S3, Glue, Redshift) or GCP (BigQuery, Dataflow);

Python — PySpark, Pandas, Boto3, Delta API ; SQL Secondary Skills: Apache Kafka / Azure Event Hubs / AWS Kinesis, Databricks Structured Streaming, Databricks MLflow, Databricks AI Assistant, Snowflake, OpenLineage / Databricks Unity Catalog lineage, Azure DevOps / GitHub Actions, Cloud Cost Management / Databricks DBU optimisation, Docker / Kubernetes, REST / GraphQL API Integration, Data Governance Frameworks, GitHub Copilot.

📌 Walk-in || Sr. Databricks Developer (Hyderabad)
🏢 Hexaware Technologies
📍 Hyderabad