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Senior level plus niche Databricks/Spark skillset yields moderate applicant density.
Core data engineering skills transfer across industries, but Databricks/Lakehouse specialization raises sensitivity to medium.
Extensive mandatory skills list and explicit 10+ years requirement make shortlisting highly strict.
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Design, develop, and support large-scale enterprise data platforms using Databricks Lakehouse technologies.
Lead technical initiatives including architecture decisions, mentoring engineers, and delivering complex data platform projects.
Build and optimize scalable batch and streaming ETL/ELT pipelines using Spark, Python, and SQL with performance tuning and cost optimization.
10+ years of hands-on data engineering experience with enterprise data platforms and proven technical leadership.
Strong expertise and production experience with Databricks Lakehouse Platform including Delta Lake, DLT, Unity Catalog, and Medallion Architecture.
Expertise in Apache Spark/PySpark, Python programming, advanced SQL, ETL/ELT pipeline development, and cloud platforms (AWS, Azure, or GCP).
Experience with workflow orchestration tools like Airflow or Databricks Workflows and implementing data governance, security, and CI/CD practices.
Experienced in leading data engineering teams with responsibilities for architecture, mentoring, and delivering large data projects.
Skilled at handling performance optimization and large-scale data migrations to modern cloud data platforms.
Familiar with modern data modeling and lakehouse architectures, with practical knowledge of GenAI/LLM concepts as a plus.