Databricks Data Engineer

Role Summary

We are looking for an experienced MDM / Databricks Data Engineer to build scalable master data and integration solutions on a modern Databricks Lakehouse. The role will focus on common IDs, Golden Records, entity resolution, data quality, governance, and cross-platform data integration.

Key Responsibilities

• Design and implement MDM solutions, including common IDs, Golden Records, entity matching, deduplication, and survivorship rules.
• Integrate data from multiple source systems into the Databricks Lakehouse.
• Develop and optimize data pipelines using Databricks, PySpark, SQL, and Delta Lake.
• Build and enhance Bronze, Silver, and Gold data layers.
• Implement data quality, standardization, reconciliation, and validation processes.
• Support Unity Catalog, data lineage, access controls, cataloguing, and data governance.
• Implement data masking and anonymization for sensitive data.
• Build trusted, reusable data layers for cross-platform analytics and intelligence.
• Collaborate with business, architecture, and engineering teams to define MDM and data integration requirements.

Required Skills

• Strong experience in Master Data Management (MDM) and data engineering.
• Hands-on expertise in Databricks, PySpark, SQL, Delta Lake, and Lakehouse architecture.
• Experience with Golden Records, common IDs, entity resolution, deduplication, and data quality.
• Good understanding of Unity Catalog, data governance, metadata, and lineage.
• Experience integrating data from multiple enterprise platforms.
• Knowledge of Medallion Architecture and data privacy/anonymization.

Good to Have

• Experience with Informatica MDM/IDMC, Reltio, Profisee, Semarchy, or similar MDM platforms.
• Cloud experience with Azure, AWS, or GCP.
• Exposure to large-scale data modernization or migration programs.