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Tier-1 brand but senior specialized Databricks role reduces applicant density.
Core Databricks/Spark skills are transferable, but senior banking context favors finance experience.
Explicit 11+ years and mandatory Databricks/Spark/AWS skills create stringent screening filters.
Lead the modernization and refactoring of legacy Spark data pipelines to native Databricks on AWS architectures, eliminating Hadoop dependencies.
Design, build, and optimize scalable data processing solutions using Apache Spark (JavaSpark/PySpark), Delta Lake, and Databricks features including orchestration and auto-scaling.
Contribute to architectural design, performance tuning, engineering standards, and cross-team technical collaboration to ensure robust, maintainable production data platforms.
11+ years of experience in data engineering or distributed systems with hands-on Apache Spark (JavaSpark/PySpark) and Databricks on AWS expertise.
Strong SQL skills and experience with AWS cloud services and large-scale distributed data processing.
Bachelor’s degree or equivalent experience.
Work Experience Required: 11+ years
Demonstrated ability to translate high-level architecture into detailed technical designs and reusable scalable data pipeline components.
Experienced in modernizing legacy data platforms to cloud-native AWS architectures with strong Spark performance optimization skills.
Proven track record working in complex, time-sensitive environments and collaborating with architects, platform teams, and DevOps globally.