We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Software Engineer at JPMorgan Chase within the JPM - US Wealth Management , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Supports review of controls to ensure sufficient protection of enterprise data
- Advise and making custom configuration changes in one to two tools to generate a product at the business or customer request also updates logical or physical data models based on new use cases
- Frequently uses SQL and understands NoSQL databases and their niche in the marketplace
- Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development also gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems.
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture.
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Experience across the data lifecycle spark-based Frameworks for end-to-end ETL, ELT & reporting solutions using key components like Spark SQL & Spark Streaming.
- Strong hands on working experience of Big Data stack including Spark and Python (Pandas, Spark SQL).
- Good understanding on RDMS database, Relational, No SQL databases and Linux/UNIX.
- Strong knowledge of multi-threading and high volume batch processing.
- Should be good in performance tuning on for Python and Spark along with Autosys or Control-M scheduler.
- Cloud implementation experience with AWS including, AWS Data Services: Proficiency in Lake formation, Glue ETL (or) EMR, S3, Glue Catalog, Athena, Kinesis (or) MSK, Airflow (or) Lambda + Step Functions + Event Bridge, Data De/Serialization: Expertise in at least 2 of the formats: Parquet, AVRO, Fixed Width, AWS Data Security: Good Understanding of security concepts such as: Lake formation, IAM, Service roles, Encryption, KMS, Secrets Manager.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
- Proficient in all aspects of the Software Development Life Cycle.
- Solid understanding of agile methodologies such as CI/CD, Applicant Resiliency, and Security.
- Knowledge of Java and Microservice architecture