Careers at Charles Schwab

Your opportunity

At Charles Schwab, our purpose is simple: we champion client's goals with passion and integrity. Guided by honesty, mutual respect and a commitment to doing what's right, we bring innovation, education, and service together to help shape financial futures. Our people are the foundation of our success - they approach their work with curiosity and collaboration, coming together to create solutions that make a meaningful impact for clients and communities. As we expand into India, we are bringing this same culture of inclusion, learning, and opportunity to new talent. Joining us means becoming part of a global team where your work matters and your future can take shape.

Our Hyderabad location is central to Schwab's growth, bringing together talented people and technology to drive innovation, scale and efficiency. Here, you will work alongside teams who create solutions that support millions of clients every day. The work you do is more than daily operations - it's a chance to experiment, learn, and build within a values-driven, supportive environment. This is a unique opportunity to be part of our early growth phase and shape something new, backed by the stability and strength of a Fortune 500 company. Your impact begins on day one, and your contributions will help define our future in the region.

What you have

Key Responsibilities

At Schwab, you are empowered to make an impact on your career. Innovative thinking meets disciplined engineering as we challenge the status quo and strengthen the quality of digital experiences that serve our clients.

This Senior Manager role will lead quality engineering and SDET practices for business-critical enterprise applications. The role combines hands-on automation architecture with technical leadership, partnering across product, development, architecture, operations, and risk teams to build quality into every stage of delivery.

The ideal candidate is a pragmatic quality leader who can independently drive large-scale initiatives, modernize automation frameworks, coach engineers, and improve release confidence through measurable, risk-based testing.

AI-enabled quality engineering is a core expectation of this position. The leader will establish practical, governed use of AI agents and coding assistants to accelerate test design, framework development, end-to-end regression automation, failure analysis, and continuous quality improvement while maintaining human review, security, privacy, traceability, and reliability controls.

• Drive quality strategy and successful execution for complex, business-critical programs, aligning test coverage, automation, release readiness, and quality outcomes with organizational objectives.

• Architect, develop, and evolve scalable UI and API automation frameworks using Playwright with JavaScript/TypeScript and Selenium with Java, applying reusable design patterns, reliable synchronization, test-data management, and actionable failure diagnostics.

• Establish an effective quality-engineering approach across functional, integration, regression, end-to-end, API, database, accessibility, compatibility, and non-functional testing, with appropriate use of exploratory and risk-based testing.

• Validate data integrity and backend processing through advanced SQL across Microsoft SQL Server and PostgreSQL, including source-to-target reconciliation, stored procedure validation, and troubleshooting of complex application workflows.

• Integrate automated tests into CI/CD pipelines and GitHub-based workflows; improve execution speed, stability, reporting, environment readiness, and defect traceability using modern test and quality-management tooling.

• Lead test planning, estimation, quality metrics, defect triage, root-cause analysis, release risk assessments, and production-quality reviews, distinguishing product defects from automation or environment instability.

• Guide and mentor SDETs and QA engineers, set coding and automation standards, conduct design and code reviews, and foster accountability, collaboration, continuous learning, and engineering excellence.

• Partner with geographically distributed product, engineering, architecture, DevOps, operations, compliance, and vendor teams to manage dependencies, resolve blockers, and deliver secure, resilient, and maintainable solutions.

• Contribute to long-term quality-engineering roadmaps, platform modernization, tooling decisions, and responsible adoption of AI-assisted testing while following data privacy and company-policy guardrails.
  • Define and lead an AI-enabled quality-engineering strategy, including the creation and governed use of specialized testing agents for requirement analysis, test-case generation, automation development, test-data preparation, code review, regression-suite maintenance, defect reproduction, and failure triage.
  • Build and maintain enterprise end-to-end regression suites and reusable test frameworks using Playwright, JavaScript/TypeScript, Java, Selenium, mabl, API automation, and AI-assisted engineering tools such as GitHub Copilot and Claude, with mandatory human validation of generated code, tests, assertions, and conclusions.
  • Create agent instructions, reusable prompts, repository context, tool integrations, and evaluation criteria that enable AI agents to consistently follow application architecture, coding standards, testing patterns, security controls, and quality gates.
  • Own GitHub engineering administration and governance for quality repositories, including repository structure, branching strategy, pull-request controls, access management, protected branches, secrets usage, dependency management, release tagging, reusable workflows, auditability, and quality dashboards.
  • Design, manage, and continuously improve CI/CD and test-execution pipelines across GitHub Actions, Jenkins, and Bamboo, including pipeline-as-code, parallel and distributed execution, environment orchestration, test selection, artifact retention, reporting, failure notifications, and release gates.
  • Lead adoption and governance of mabl for agentic and low-code test automation, including browser and API coverage, reusable flows, test plans, environment configurations, CI/CD integration, intelligent test maintenance, and alignment with the broader Playwright and Selenium automation strategy.
  • Establish controls for responsible AI use in quality engineering, including approved-tool usage, data classification and privacy protection, restricted handling of credentials and production data, prompt and output review, model-risk awareness, reproducibility, evidence retention, and monitoring for inaccurate or unsafe AI-generated results.
  • Define measurable AI and automation outcomes such as regression coverage, execution duration, flaky-test rate, escaped defects, failure-triage time, maintainability, adoption, and productivity, using metrics to guide investment and continuous improvement.
    
    Required Qualifications
    
    
  • • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
    • 12+ years of experience in software quality engineering, QA automation, SET, or SDET roles, including 5+ years leading engineers or major quality-engineering initiatives for enterprise applications.
    • Advanced hands-on experience designing and maintaining automation frameworks with Playwright and JavaScript/TypeScript, plus strong experience with Java and Selenium WebDriver.
    • Strong experience testing web applications, REST APIs, microservices, and distributed systems using appropriate automation libraries and API tools such as REST Assured, Postman, or equivalent.
    • Proficiency in SQL and database validation across Microsoft SQL Server and PostgreSQL, including complex queries, data reconciliation, backend troubleshooting, and performance-aware test design.
    • Experience integrating automated suites with CI/CD and source-control workflows using GitHub, GitHub Actions, Jenkins, AWS DevOps, or comparable platforms; practical knowledge of containers and cloud-based test execution.
    • Demonstrated knowledge of Page Object or Screenplay patterns, test pyramids, contract testing, mocking or service virtualization, TDD/BDD, parallel execution, cross-browser testing, and dependable test-data strategies.
    • Experience with Jira, Confluence, Xray, Zephyr, TestRail, qTest, or equivalent, along with clear quality metrics and executive-ready reporting.
    • Working knowledge of accessibility, security, performance, and observability testing, JMeter or k6, Splunk, and application-log analysis.
    • Proven ability to work independently, lead multiple large-scale efforts, influence technical decisions, communicate risks clearly, and collaborate effectively in Agile environments.
    • Mandatory hands-on experience applying generative AI and agentic AI to software quality engineering, including the creation or configuration of testing agents and the use of AI-assisted tools such as GitHub Copilot, Claude, or comparable enterprise-approved platforms to design tests, generate and review automation code, create end-to-end regression suites, maintain frameworks, and analyze failures.
    • Demonstrated ability to evaluate AI-generated tests and code for functional correctness, determinism, security, privacy, maintainability, coverage, bias, hallucination risk, and compliance with enterprise engineering standards; candidates must be able to keep a human-in-the-loop for material quality and release decisions.
    • Advanced ownership of GitHub repository management and governance, including permissions, branching, pull requests,  protected branches, secrets, reusable workflows, dependency controls, release management, auditability, and integration with enterprise CI/CD and quality tooling.
    • Strong experience designing and operating production-grade automation pipelines with GitHub Actions, Jenkins, and Bamboo, including pipeline-as-code, parallel execution, environment and test-data orchestration, reports and artifacts, notifications, quality gates, failure recovery, and troubleshooting.
    • Hands-on experience with mabl or a comparable intelligent test-automation platform, including authoring and maintaining browser and API tests, reusable flows, execution plans, environment configuration, CI/CD integration, result analysis, and governed use of AI-assisted capabilities.
    • Experience integrating AI agents with test-management, observability, source-control, CI/CD, and engineering workflows through APIs, webhooks, or Model Context Protocol (MCP).
    • Experience establishing reusable AI prompts, agent instructions, contextual grounding, evaluation datasets or benchmark scenarios, and acceptance criteria for AI-assisted testing workflows, with clear versioning and traceability in source control.
      
      Preffered Qualification
      
      
      • Experience in financial services, brokerage, wealth management, or another highly regulated industry.
      • Experience modernizing legacy Selenium suites to Playwright and improving flaky-test detection, execution time, coverage, and maintainability.
      • Knowledge of AWS, GCP, or Azure; Kubernetes/Docker; mobile automation with Appium; event-driven systems; and data-pipeline testing.
      • Practical use of AI-assisted engineering tools to accelerate test design, automation development, code review, and failure triage, with human validation and appropriate privacy controls.
      • Relevant quality, Agile, cloud, security, or testing certifications.
      • Experience defining an enterprise adoption roadmap for AI-enabled testing, including proof-of-value criteria, tool evaluation, licensing and usage controls, training, reusable assets, governance, and scaled rollout across multiple teams.
      • Knowledge of testing AI-enabled application features, including validation of model responses, grounding, prompt behavior, safety controls, non-deterministic outcomes, latency, reliability, and regression evaluation.