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Metro-based Databricks data engineer role with broad, popular skillset increases applicant competition.
Databricks-focused data engineering skills are transferable across industries but have moderate platform specificity.
Explicit 7–9 years plus 3–4 years Databricks and mandatory tech stack make filters stringent.
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Design, build, optimize large-scale ETL/ELT data pipelines and lakehouse solutions on Databricks using PySpark, Spark SQL, and Delta Lake for both batch and streaming workloads.
Implement and manage data governance, access control, observability, and pipeline orchestration using Unity Catalog, Lakeflow/Delta Live Tables, and Databricks Workflows across multiple clouds.
Optimize Spark jobs and clusters for performance and cost efficiency; collaborate with stakeholders and mentor junior engineers while ensuring production-grade data quality and reliability.
7–9 years of Data Engineering experience with at least 3–4 years hands-on production experience on Databricks.
Strong programming skills in Python and/or Scala; deep expertise with PySpark, Spark SQL, and Delta Lake.
Proficiency in at least one major cloud platform (AWS, Azure, or GCP) and experience integrating Databricks with cloud-native services.
Strong SQL skills including query optimization and experience with streaming technologies (Structured Streaming, Kafka, Event Hubs, or Kinesis).
Advanced familiarity with Databricks platform including Unity Catalog, Lakeflow/Delta Live Tables, Databricks Workflows, and emerging features (Lakebase, Mosaic AI, Agent Bricks).
Experience implementing data engineering best practices including CI/CD pipelines, medallion architecture data models, and data quality observability frameworks.
Capable of independently troubleshooting, performance tuning, and mentoring in a mature data engineering environment with production-grade pipelines.