Description Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team. As a Data Engineer III at JPMorganChase within the External Regulatory Financial Control (ERFC) Technology team, you will play a crucial role in designing, developing, and maintaining scalable data pipeline solutions using Databricks, Python/PySpark on AWS. You will collaborate with cross-functional teams to deliver high-quality data pipelines that support our business objectives. Job responsibilities Design, develop, and maintain robust data pipelines using Python and PySpark on Databricks platform on AWS Process and transform large-scale financial datasets, implementing big data processing techniques to produce aggregated financial data for analytics and reporting Optimize complex queries and data processing workflows to ensure efficient performance at scale Analyze aggregated data outputs to identify data quality issues, anomalies, and processing bottlenecks, implementing corrective solutions Participate in the full Software Development Life Cycle (SDLC), including requirements gathering, design, development, testing, deployment, and maintenance Implement data quality checks, monitoring, and alerting mechanisms to ensure data accuracy and pipeline reliability Work with our partners Product Owners and end users to support their business use cases Act as both Production Support and SRE function as part of the Data Engineer role Utilise AI tools to quickly build and test new data pipelines (e.g. CoPilot, Claude Code) Required qualifications, capabilities, and skills Strong hands-on experience in data engineering or related roles Strong proficiency in Python and PySpark for large-scale data processing Demonstrated experience with Databricks platform and Apache Spark ecosystem Proven track record of building and optimizing data pipelines for big data workloads Strong SQL skills with experience in query optimization and performance tuning Experience with AWS cloud services (S3, ECS, SNS/SQS, Lambda, etc.) Strong analytical skills with ability to investigate data issues, identify root causes, and implement solutions Experience with the complete SDLC, Jules/Jenkins, Spinnaker, Sonar and Agile methodologies Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field Preferred qualifications, capabilities, and skills Experience working with financial data and understanding of data aggregation techniques Experience with data orchestration tools (Airflow, Step Functions, etc.) Understanding of financial services industry and regulatory requirements Databricks or AWS certifications Automated testing frameworks, e.g. Playwright, Cucumber, Gherkin etc. Experience with Parquet, JSON, CSV, Avro, Delta Lake