Data Engineer (Software Engineer II)

JOB DESCRIPTION

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you.

As a Software Engineer II at JPMorganChase within the Workforce Technology, Employee Platforms team, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

• Executes standard software solution, design, development, and technical troubleshooting. Manage data integration and data analysis of disparate systems
• Build extensible data acquisition and integration solutions to meet the functional and non-functional requirements of the client. Implement processes and logic to extract, transform, and distribute data across one or more data stores from a wide variety of sources
• Provide problem-solving expertise and complex analysis of data to develop business intelligence integration designs
  Interface with other internal product development teams as well as cross functional teams (Product Management, Integration Engineering, Quality Engineering, System Admin Teams)
• Working with remote and geographically distributed teams to enable building the right products, using the right building blocks, and making them consumable by other products easily
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
• Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development

Required qualifications, capabilities, and skills

• 4+ years experience in data integration projects using Big Data Technologies. Experience in AWS Data Warehousing and database platforms with hands-on delivery using EMR, S3, AWS Glue, Lambda, Apache Airflow and Infrastructure as Code (IaC) (e.g., Terraform/CloudFormation)
• Strong experience in CICD using Jenkins, Git, Artifactory, Yaml, Maven for Cloud deployments
• Hands on experience on Spark engineering with PySpark/Scala for building and optimizing scalable data pipelines. Good knowledge of Big Data querying tools, such as Athena, RDS, Databricks SQL Warehouse.
• Strong experience in integrating data from different types of file-storage formats like Parquet, ORC, Avro and table formats like Delta Lake or Iceberg
• Strong knowledge of Data Warehousing, Data Modeling and Data Lake concepts
• Strong experience with Java open-source and API standards
• Hands-on practical experience in system design, application development, testing, and operational stability
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.

Preferred qualifications, capabilities, and skills

• Experience in technologies like Oracle/SQL and NoSQL data stores such as DynamoDB
• Ability to quickly learn new technologies in a dynamic environment
• Databricks preferred

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