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Databricks specialization narrows the candidate pool despite a metro location.
Databricks and AWS skills are transferable across industries, but platform-specific expertise raises moderate domain bias.
Explicit 6–8 years plus mandatory Databricks, PySpark, Unity Catalog, and AWS requirements increase filter strictness.
Design and develop scalable batch and real-time data pipelines using Databricks, PySpark, SQL, and AWS cloud technologies.
Implement data governance and access controls with Unity Catalog and develop data models and warehousing solutions.
Collaborate with stakeholders to translate business requirements into technical solutions and drive best practices in code quality, CI/CD, and testing.
6–8 years of Data Engineering experience.
Strong hands-on expertise with Databricks, Delta Lake, Unity Catalog, PySpark, and SQL.
Experience with AWS cloud services including S3, IAM, Lambda, EC2, and Redshift.
Bachelor’s or Master’s degree in Engineering, Technology, or Computer Applications (BE/B.Tech/M.Tech/MCA or equivalent).
Experienced in designing and optimizing high-performance data pipelines in cloud environments using Databricks and AWS.
Skilled in data governance frameworks and managing access control in enterprise data platforms.
Capable of mentoring peers and leading continuous improvement initiatives while collaborating effectively with stakeholders.