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Niche senior Databricks specialization with mandatory certifications reduces applicant density.
Deep Databricks, Spark, Delta, and Unity Catalog expertise limits transferability across platforms.
Mandatory 12+ years and Databricks certification plus extensive platform skills create strict filters.
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Architect and lead enterprise Databricks Lakehouse platform design and deployment on Azure, AWS, or GCP with focus on multi-region, workspace federation, and identity federation.
Lead migration of legacy data warehouses to Databricks including SQL translation, workload profiling, and cost modeling; own cluster architecture and pipeline optimization for cost and performance.
Define and implement Data Governance (Unity Catalog), data mesh patterns, ML platform design using Databricks ML, as well as lead stakeholder engagements and technical mentorship.
12+ years in data architecture, data engineering, or analytics with minimum 5 years focused on Databricks platform delivery.
Databricks Certified Data Engineer Professional or Certified Associate Developer for Apache Spark required.
Expertise in Apache Spark (PySpark, Scala) including performance tuning and Delta Lake with production Delta Live Tables experience.
Strong experience deploying Databricks on at least one major cloud (Azure, AWS, or GCP) including infrastructure-as-code (Terraform) and MLflow usage.
Senior-level architect with extensive hands-on experience across Databricks platform components and complex enterprise-scale data environments.
Proven capability in migrating legacy data warehouses to Databricks and designing scalable, governed data lakehouse architectures.
Experienced leader and mentor comfortable engaging C-suite stakeholders and defining enterprise best practices and center-of-excellence standards.