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Tier-1 brand, metro location, common data-engineer title and broad Databricks/AWS skillset increase applicant density.
Databricks, Spark, and AWS data engineering skills are broadly transferable across industries.
Explicit 11+ years and mandatory Databricks/Spark/Delta/AWS experience create strict screening filters.
Lead the modernization and continuous evolution of data processing pipelines from legacy Hadoop to Databricks on AWS.
Design, implement, and optimize Spark (JavaSpark/PySpark) solutions using Databricks native features including Delta Lake and workflow orchestration.
Contribute to architecture and design decisions ensuring scalability, maintainability, and performance of data platform solutions.
11+ years of experience in data engineering or distributed systems.
Strong expertise in Apache Spark (JavaSpark/PySpark), Databricks on AWS, and Delta Lake.
Experience with SQL and AWS large-scale distributed data processing services.
Bachelor’s degree or equivalent experience.
Proven capability to refactor legacy data platforms into cloud-native architectures with modernization focus.
Experienced in translating high-level architecture into detailed technical designs and reusable pipeline components.
Able to work in high-impact, time-bound environments providing technical leadership and collaborating across global teams.