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Niche Databricks certification and deep senior consulting requirements reduce candidate density.
Strong Databricks, Spark, and cloud specialization makes cross-industry transferability limited.
Mandatory Databricks certification, 6–8 completed implementations and 10+ years create highly stringent filters.
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Design, build, and optimize scalable large-scale data engineering solutions using the Databricks Data Intelligence Platform.
Work closely with customers and stakeholders to translate business requirements into technical solutions across AWS, Azure, and/or GCP clouds.
Lead Databricks implementations including Spark-based ETL pipelines, CI/CD, workload optimization, and support for MLOps workflows.
10+ years total work experience with at least 7 years in data engineering, data platforms, and analytics.
Databricks Data Engineering Professional Certification is mandatory.
Completed 6 to 8+ full Databricks implementation projects with hands-on development experience.
Strong expertise in Apache Spark, distributed computing, cloud platforms (AWS/Azure/GCP), CI/CD pipelines, and MLOps practices.
Experienced consultant or solutions architect with demonstrated delivery of enterprise-scale Databricks implementations (6+ projects).
Deep technical expertise in Spark internals, performance tuning, and cloud-native data engineering architectures.
Comfortable collaborating with customer stakeholders and providing technical leadership on Databricks and cloud data platforms.