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Recognizable financial brand and popular data-engineer title, but senior specialization reduces applicant density.
Technical data engineering skills are transferable, though financial domain preference raises sensitivity to medium.
Explicit 10+ years requirement plus mandatory Databricks, Spark, Scala, and Azure skills increases shortlisting rigor.
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Design, develop, and maintain scalable data pipelines and ETL frameworks using Databricks, Apache Spark, and Scala.
Optimize Spark jobs and cluster utilization for performance, scalability, and reliability in Azure cloud environments.
Troubleshoot production issues, manage workflows, and collaborate with cross-functional teams to deliver high-quality data solutions.
10+ years of experience in Data Engineering, with 6+ years hands-on Databricks and Spark development.
Strong hands-on skills in Databricks, Apache Spark, Scala, Spark SQL, PySpark, and Azure cloud services including ADLS and ADF.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
Strong experience in developing production-grade Scala applications and SQL query optimization.
Experienced in managing large-scale distributed data processing and building scalable, cloud-native ETL pipelines on Microsoft Azure.
Proficient in performance tuning, job orchestration, and implementing CI/CD best practices for data engineering workflows.
Comfortable working in Agile environments and collaborating with cross-functional teams delivering data products at scale.