JPMorgan Chase is looking to advance data engineering by developing innovative, secure solutions that transform financial services. The Corporate Data Technology Team needs a Lead Data Engineer to enhance, build, and deliver trusted market-leading technology products securely, stably, and scalably.
Requirements
- Advanced proficiency in data processing frameworks and tools, including Parquet, Iceberg, PySpark, Glue, Lambda, Databricks, and AWS data services (EMR, Athena, Redshift).
- Proficiency in programming languages such as Python, Java, or Scala for data processing and application development.
- Extensive hands-on experience with Databricks, including architecting and managing large-scale data pipelines, optimizing Spark jobs, implementing security and governance controls, and automating workflows. Proven ability to leverage Databricks for advanced analytics, data lakehouse architectures, and seamless integration with AWS or other cloud services.
- Proficiency in automation and continuous delivery methods, utilizing CI/CD pipelines with tools like Git/Bitbucket, Jenkins, or Spinnaker for automated deployment and version control.
- Hands-on practical experience delivering system design, application development, testing, and operational stability, with advanced understanding of agile methodologies, application resiliency, and security.
- Demonstrated proficiency in software applications and technical processes within technical disciplines such as cloud, artificial intelligence, machine learning, and mobile.
- Leverage AI agents and tools, such as Co-Pilot, to enhance productivity, code quality, and problem-solving in engineering tasks.
Responsibilities
- Execute creative software and data solutions, including design, development, and technical troubleshooting, by thinking beyond routine or conventional approaches to build solutions or break down technical problems.
- Develop secure, high-quality production code and data pipelines, reviewing and debugging processes implemented by others.
- Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability of software applications and systems.
- Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
- Work with business stakeholders to understand requirements and design appropriate solutions, producing architecture and design artifacts for complex applications.
- Implement robust monitoring and alerting systems to proactively identify and address data ingestion issues, optimizing performance and throughput.
- Implement data quality checks and validation processes to ensure accuracy and reliability of data.
Other
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- 7+ years of experience in data engineering, including design, application development, testing, and operational stability.
- Experience with scheduling tools like Autosys or Airflow to automate and manage job scheduling for efficient workflow execution.
- AWS Certifications.
- Databricks Certifications.
