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Niche Databricks specialization reduces pool, but mid-level metro role increases applicant density.
Low—Databricks, PySpark, and Azure skills transfer well across industries.
High due to explicit 4–6 years requirement and mandatory Databricks/PySpark production experience.
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Design, develop, and implement efficient and reliable data pipelines using Databricks Delta Live Tables and PySpark for structured and unstructured data supporting AI/ML downstream consumption.
Develop and optimize complex SQL queries and data models (fact/dimension tables, domain data marts) in Databricks, ensuring data governance with Unity Catalog.
Build and manage Databricks workflows including orchestration, CI/CD deployment, workspace administration, and monitoring pipeline health and resource usage.
3+ years hands-on experience with PySpark, Delta Lake, Workflows, Unity Catalog, and Databricks in production environment.
4-6+ years overall data engineering experience with demonstrated ability to build and deliver pipelines, not just support them.
2+ years hands-on Azure Databricks experience in production environments.
Experience with multiple data source types including relational, file-based, and API.
Experienced data engineer comfortable working within and improving defined architectures using Databricks ecosystem.
Skilled in building production-grade data pipelines and transformations prioritizing optimization, cost-efficiency, and operational monitoring.
Capable of collaborating effectively with data scientists, analysts and engineering teams to deliver scalable, governed, and reusable data solutions.