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Databricks specialization reduces competition, but metro location and known employer drive moderate applicant density.
Platform-focused Databricks and ML pipeline skills are transferable across industries but require domain-specific experience.
Explicit 6+ years plus mandatory Databricks, PySpark, ML pipeline and cloud experience increases shortlisting strictness.
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Design, build, and maintain scalable data and ML pipelines using Python, SQL, and Databricks tooling to support analytics and machine learning.
Own core data platform infrastructure such as Delta Lake architecture, Unity Catalog governance, Databricks Workflows orchestration, and compute optimization.
Collaborate with data scientists and stakeholders to operationalize ML models and define data platform standards, ensuring reliability, performance, and governance.
6+ years of experience in data engineering, data platform, or ML engineering roles.
Strong proficiency in Python and SQL with proven experience building production-grade data pipelines.
Hands-on expertise with Databricks technologies including Delta Lake, Unity Catalog, Databricks Workflows, and PySpark.
Experience operating cloud data platforms (AWS, Azure, or GCP).
Experienced in both data engineering and ML platform operations with the ability to bridge experimentation and production ML systems.
Proficient in managing large-scale batch and streaming data pipelines with a strong focus on reliability, cost optimization, and scalability.
Capable of defining and enforcing data modeling, governance, and engineering standards within a cross-functional team environment.