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Tier-1 employer and metro location increase competition, but senior Databricks specialization reduces applicant density.
Highly specialized Databricks, Spark, and AWS data engineering skills limit cross-industry portability.
Explicit 10+ years and mandatory Databricks/Spark/Delta Lake expertise make screening highly selective.
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Lead modernization and refactoring of legacy Spark pipelines to Databricks native architectures on AWS.
Architect, design, and optimize scalable, maintainable, and performant data pipelines using Apache Spark and Databricks features like Delta Lake and Workflows.
Collaborate with architects and platform teams to implement engineering standards and support production deployments with testing and troubleshooting.
10+ years of experience in data engineering or distributed systems.
Strong expertise in Apache Spark (JavaSpark / PySpark), Databricks on AWS, and Delta Lake.
Experience with AWS services and large-scale distributed data processing.
Bachelor’s degree or equivalent experience.
Experienced in modernizing legacy Hadoop platforms to cloud-native AWS architectures using Databricks.
Strong hands-on Spark engineer capable of contributing to architectural decisions and delivering complex optimizations.
Demonstrated ability to produce scalable, maintainable, and production-ready data solutions in high-impact environments.