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Tier-1 employer and metro location increase competition, though niche Databricks/Scala experience narrows the pool.
Core data engineering skills are transferable, but Databricks/Scala and financial domain knowledge increase specificity.
Explicit 10+ years and 6+ years Databricks/Spark/Scala requirements enforce strict screening.
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Design, develop, and maintain scalable data pipelines using Databricks, Apache Spark, and Scala for large-scale distributed data processing.
Optimize Spark jobs and cluster usage for performance, scalability, and reliability in Azure cloud environments.
Manage Databricks workflows, job orchestration, troubleshoot production issues, and collaborate in Agile teams to deliver production-grade data products.
10+ years of Data Engineering experience with 6+ years hands-on in Databricks and Spark development.
Strong expertise in Databricks, Apache Spark, Scala, Spark SQL, PySpark, and Microsoft Azure platform including ADLS and ADF.
Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Experience with ETL/ELT pipeline development, distributed data processing frameworks, and SQL query optimization.
Experienced in building production-grade Scala applications and optimizing Spark workloads in cloud-native (Azure) environments.
Skilled in developing scalable, performant data architectures and frameworks within Financial Services or Reference Data domains.
Familiar with CI/CD pipelines (Azure DevOps, Harness), Delta Lake, Unity Catalog, and using source control tools like Git/GitHub.