Java Developer

Charles Schwab Inc.•Southlake, TX

3d•$48 - $56•Onsite

About The Position

As a Java Developer supporting the Enterprise Security Master (ESM) platform, you will help power one of Schwab’s mission-critical applications that serves as the source of record for securities reference and pricing data across the firm. ESM processes and distributes high-volume asset data that supports downstream applications responsible for servicing clients and managing financial products. In this role, you will leverage modern software engineering practices and AI-assisted development tools to design, build, test, and support highly available, scalable distributed systems. Working closely with product owners, business stakeholders, and engineering teams, you will translate complex business requirements into secure, reliable software solutions that improve operational efficiency and business outcomes. You will play a key role in analyzing and resolving production issues, enhancing platform capabilities, and ensuring system reliability through robust testing, monitoring, and deployment practices. Success in this role requires strong problem-solving skills, sound engineering judgment, and the ability to evaluate and validate AI-generated outputs while maintaining high standards for quality, security, and performance. You will contribute to the evolution of AI-assisted software development practices, champion innovation, and collaborate across teams to deliver solutions that support Schwab’s growing technology ecosystem. This is an opportunity to influence the future of software engineering by combining technical expertise, distributed systems knowledge, and emerging agentic AI capabilities to drive meaningful business impact.

Requirements

3+ years of experience in Java development, software engineering, or application support within enterprise environments.
Hands-on experience using AI-assisted development tools such as GitHub Copilot, Claude Code, or similar technologies throughout the software development lifecycle.
Experience designing, building, testing, and supporting software applications using modern engineering best practices.
Working knowledge of distributed systems, scalable application architectures, and cloud-based design principles.
Experience troubleshooting and resolving technical issues within production environments.
Understanding of software quality practices including unit testing, regression testing, test automation, and validation methodologies.
Knowledge of CI/CD practices, release management, and deployment automation.
Experience collaborating with cross-functional teams to gather requirements and deliver business-focused technology solutions.
Understanding of application security principles, compliance requirements, and secure coding practices.
Strong analytical, problem-solving, and communication skills with the ability to translate technical concepts to diverse audiences.

Nice To Haves

Experience developing or supporting large-scale financial services, securities, market data, or enterprise data management platforms.
Familiarity with agentic AI workflows, prompt engineering, custom instructions, and AI-enabled automation.
Experience building or supporting real-time data processing, data integration, or data pipeline solutions.
Demonstrated ability to perform root-cause analysis and drive resolution of complex technical challenges.
Experience mentoring team members and promoting engineering best practices.
Knowledge of monitoring, observability, and operational support frameworks.
Ability to evaluate business needs, identify improvement opportunities, and recommend innovative solutions.
Strong written and verbal communication skills with the ability to influence peers and stakeholders.
Demonstrated adaptability, continuous learning mindset, and passion for emerging technologies.
Experience working in Agile software development environments.

Responsibilities

Design, build, test, and support highly available, scalable distributed systems.
Translate complex business requirements into secure, reliable software solutions.
Analyze and resolve production issues.
Enhance platform capabilities.
Ensure system reliability through robust testing, monitoring, and deployment practices.
Evaluate and validate AI-generated outputs.
Contribute to the evolution of AI-assisted software development practices.
Collaborate across teams to deliver solutions.