Software Engineer III – Fullstack Java

Overview

As a Full Stack Software Engineer III in the Asset Management Team, you design and deliver secure, scalable tech products that support key business functions. You will collaborate with cross-functional teams to solve complex problems, improve delivery, and uphold high-quality outcomes, shaping the technology landscape with innovation. You work across multiple technical areas to support enterprise objectives and drive resilient software solutions. You will leverage AI-assisted development to boost automation and code quality, while ensuring security and scalability at scale.

Responsibilities

• Execute software solutions and contribute to design, development, and troubleshooting
• Create secure, high-quality production code and maintain algorithms
• Leverage AI coding assist tools to improve quality, speed, and productivity
• Validate outputs via peer reviews, automated testing, and secure coding standards
• Contribute learnings and reusable patterns to improve team effectiveness
• Apply SDLC toolchain knowledge to development
• Utilize AI-assisted development and automation to maximize value
• Produce architecture and design artifacts for complex applications
• Ensure design constraints are met during coding
• Identify hidden data patterns to drive improvements
• Enhance coding hygiene and system architecture

Key requirements

• Formal training or certification in software engineering concepts and advanced applied experience
• Strong hands-on experience with Java
• Experience with data distribution and consumption using event, Kafka streaming, API (GraphQL and REST), and bulk methods
• Public cloud experience, AWS, with knowledge of mesh data distribution using Snowflake and Starburst compute engines
• Hands-on experience in system design, development, testing, and operational stability
• Experience coding in a large corporate environment with modern languages and database querying
• Experience using enterprise-authorized AI-assisted development tools for coding, testing, or documentation
• Ability to critically evaluate and refine AI-generated outputs for correctness, performance, and security
• Understanding of responsible AI use in engineering workflows, data handling, resiliency and security expectations
• Understanding of the Software Development Life Cycle and agile methodologies (CI/CD, resiliency, security)

• collaboration
• problem solving
• communication
• Java
• Kafka
• GraphQL

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