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Mid-level metro role with common data skills but Databricks specificity yields moderate competition.
Databricks, cloud and data platform skills are highly transferable across industries.
Explicit 5+ years, 3+ years Databricks experience and certification requirement makes screening strict.
Design, build, operate, and continuously improve an enterprise-scale Databricks based data mesh platform focusing on reliability, scalability, security, and interoperability.
Enable data engineers, analytics, and data science teams with domain autonomy for ingesting, processing, and sharing data securely using Databricks.
Deliver POCs, MVPs, experiments, and technology evaluations using design thinking to prioritize complex business initiatives and manage stakeholder communication.
5+ years experience in data engineering, data platform engineering, or architecture.
3+ years hands-on production experience with Databricks.
B.tech or M.tech degree.
Databricks Certified Professional certification required.
Expert-level knowledge of Databricks Lakehouse, Spark SQL, Delta Lake, Unity Catalog, and proficient in Python, PySpark, or Scala for ETL/ELT development.
Experience with major cloud platforms, preferably AWS, and familiarity with Terraform, CI/CD pipelines, and DevOps practices.
Skilled in implementing data governance practices including data lineage, PII anonymization, and data quality frameworks, and experienced in leadership and cross-functional stakeholder management.