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Tier-1 brand, metro location, mid-level generalist data role, and broad skill requirements increase competition.
Core Databricks and data engineering skills are transferable, though financial domain experience is preferred.
Explicit 7+ years overall plus 5+ years Databricks, cloud and PySpark requirements make filters strict.
Design, develop, and deploy high throughput, configurable data pipelines using Databricks for processing large and near real-time data volumes.
Optimize Databricks job performance including query tuning, resource management, and scaling strategies.
Fully manage software development lifecycle tasks including design, coding, integration testing, deployment, and documentation for data engineering solutions.
7+ years of overall IT experience.
5+ years of hands-on Data Engineering experience handling large datasets using Databricks SQL & Scala/PySpark.
5+ years of cloud-based development experience on Azure/AWS.
Bachelor’s Degree in computer science or IT-related discipline.
Expertise in end-to-end data solutioning for building Data Lakehouse on Databricks within cloud environments.
Strong troubleshooting skills in DevOps pipelines and Azure services with experience in Microservices and API/Event driven architecture.
Experience working in financial industry and collaborating with geographically distributed teams under agile methodologies.