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Tier-1 brand, metro location, mid-level range, and broad Databricks skillset increase competition.
Databricks/cloud specialization increases domain bias, though core data engineering skills remain transferable.
Explicit 5+ years plus mandatory Databricks, PySpark, Delta Lake, cloud and CI/CD skills impose strict filters.
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Design, develop, and maintain scalable data engineering solutions and pipelines on the Databricks platform using PySpark and SQL.
Implement, optimize, and manage data architecture and workflows including Delta Lake, Delta Live Tables, Unity Catalog, and serverless compute within cloud environments (Azure, AWS, GCP).
Collaborate with stakeholders to translate business requirements into secure, reliable, and high-performance data solutions following Lakehouse, Medallion, and Data Warehousing standards.
5+ years of relevant experience in data engineering with strong hands-on Databricks, PySpark, and SQL skills.
Experience working with cloud platforms such as Azure, AWS, or GCP is mandatory.
Bachelor’s or master’s degree in any field.
Experience with data architecture concepts including Delta Lake, Delta Live Tables, Unity Catalog, and knowledge of DevOps, CI/CD pipelines, and Agile methodologies.
Mid to senior-level data engineer skilled in designing end-to-end data solutions leveraging Databricks and cloud-native services with operational ownership.
Experience implementing Lakehouse architecture and performance tuning for large-scale data pipelines in enterprise environments.
Proven ability to manage data governance, security, and compliance implementation and collaborate effectively with cross-functional teams to deliver business-aligned data platforms.