Software Engineer III - Data Engineering | Infrastructure Platforms

JPMorganChase•Jersey City, NJ

About The Position

As a Software Engineer III in JPMorganChase’s Infrastructure Platforms organization, you will serve as a seasoned member of an agile team, designing and delivering secure, stable, scalable technology products. You will develop, test, deploy, and improve production-ready applications that combine Java software engineering with Python AI/ML capabilities, supporting business objectives across cloud and hybrid data environments. The role involves translating business problems into technical solutions, contributing to metrics reporting and dashboards for Operations Management, and helping ensure conformance with controls, policies, and procedures. You will work across the delivery lifecycle, supporting reliable deployments, monitoring, validation, and operational improvement. You will also use enterprise-authorized AI coding assistance responsibly, validate its output, and share reusable practices that improve team effectiveness.

Requirements

Formal training or certification in software engineering concepts and at least 3 years of applied experience.
Practical experience in system design, application development across the SDLC, unit and integration testing, and operational stability for enterprise cloud-native applications.
Understanding of software engineering fundamentals, APIs, source control, testing, debugging, and continuous integration and delivery practices.
Experience developing, debugging, and maintaining code in a large corporate environment using Java, Python, and SQL.
Experience with frameworks such as Spring Boot, Hibernate, and Envers.
Experience with data-intensive and statistical applications, such as Jupyter notebooks.
Data engineering experience with Big Data, Data Mesh, Data Lake architecture, relational or graph databases, PL/SQL, data analytics, and dashboards.
Experience with telemetry systems and tools such as Splunk and Elasticsearch.
Solid understanding of Retrieval-Augmented Generation (RAG), agentic workflows, prompt engineering, and model development and testing.
Hands-on experience with enterprise-authorized AI-assisted software development tools, including critically evaluating, validating, and refining generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; ability to guide peers in safe and effective use.
Exposure to cloud or hybrid platforms such as AWS and modern data or container platforms such as Databricks or Kubernetes.

Nice To Haves

Bachelor’s degree in Computer Science, with specialization.
Experience with data analytics.
Experience with Oracle, MongoDB, or similar databases.
Experience deploying a vector database on services such as Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS).

Responsibilities

Translate stakeholder and business requirements into technical solutions, prototypes, and delivery plans.
Develop secure, high-quality production code using Java and Python, and maintain algorithms that run synchronously with appropriate systems.
Use SQL to analyze large, heterogeneous datasets, validate assumptions, generate insights, and measure solution impact.
Contribute to scalable, resilient architectures and support deployment, monitoring, validation, troubleshooting, and iterative improvement.
Automate operational processes and help eliminate recurring issues to improve platform stability and efficiency.
Create unit and integration tests and participate in code reviews and continuous validation practices.
Collaborate with product owners, operations, marketing, engineering, data, and other cross-functional teams to deliver solutions aligned with enterprise standards.
Produce technical documentation, reports, and presentations for technical and non-technical audiences.
Document solution design and provide technical input to risk, privacy, controls, model, and data governance assessments.
Use enterprise-authorized AI coding-assist tools to improve code quality, delivery speed, and productivity, validating outputs through peer review, automated testing, and secure coding standards.
Contribute learnings and reusable patterns to improve team effectiveness, and apply SDLC toolchain and AI-assisted development capabilities to improve automation outcomes.
Support the design and development of metrics reporting and dashboards that enable Operations Management to execute strategic objectives and conform with controls, policies, and procedures.