S&P Global
Princeton, New Jersey
This job is with S&P Global, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. About the Role: Grade Level (for internal use): 13 Role Summary We are seeking a Technical Team Lead - AI, AWS, Java Full-Stack, Financial Platforms to lead the design, development, and delivery of index calculation and back testing platform. This role combines hands-on Java full-stack engineering, AWS cloud development, AI-assisted software delivery, and technical leadership across a financial technology platform. The Technical Team Lead will guide a team of engineers in building scalable backend services, modern user interfaces, financial calculation workflows, back testing capabilities, data integrations, automated testing frameworks, and AI-assisted QA/evaluation routines. The role requires strong technical judgment, practical leadership, and the ability to translate financial methodology requirements into reliable, auditable, and production-ready software. This is a hands-on leadership role. The successful candidate will be expected to define technical direction, mentor developers, review architecture and code, collaborate with business and quantitative stakeholders, and contribute directly to critical platform components. Key Responsibilities Technical Leadership Lead the engineering delivery of an AI-enabled financial platform for index calculation, options analytics, back testing, and workflow execution. Define technical architecture, implementation standards, coding practices, testing expectations, and delivery patterns for the engineering team. Guide developers through complex design decisions involving Java services, frontend architecture, AWS workflows, data integration, AI-assisted development, and calculation accuracy. Partner with product owners, quantitative analysts, QA teams, infrastructure teams, data teams, and business stakeholders to convert requirements into clear technical plans. Lead design reviews, code reviews, sprint technical planning, production readiness reviews, and technical risk assessments. Mentor engineers in Java full-stack development, cloud-native design, financial calculation systems, automated testing, and responsible use of AI-assisted engineering tools. Ensure the platform is scalable, secure, maintainable, observable, auditable, and aligned with financial methodology and operational requirements. Hands-On Java Full-Stack Development Design and develop backend services using Java, Spring Boot, REST APIs, and enterprise application patterns. Build platform components for index calculation, backtesting, data processing, workflow orchestration, exception handling, validation, and reporting. Implement financial calculation logic based on methodology specifications, including options-based strategies, rebalancing rules, pricing inputs, market calendars, and historical backtesting assumptions. Develop modern frontend applications using React, Angular, Vue, TypeScript, JavaScript, HTML, and CSS. Build user interfaces for index setup, backtest configuration, workflow monitoring, calculation review, validation results, exception management, dashboards, and reporting. Ensure strong integration between frontend applications, backend APIs, authentication flows, data services, and cloud workflows. AI-Assisted Engineering and Spec-Driven Development Apply Spec-Driven Development practices to convert financial methodology documents, business requirements, and technical specifications into testable software components. Use AI-assisted engineering workflows to support planning, code generation, refactoring, test creation, documentation, and quality review. Review AI-generated or AI-assisted code for correctness, maintainability, security, performance, test coverage, and alignment with platform standards. Help establish team practices for responsible AI-assisted development, including review checklists, validation gates, test coverage expectations, and documentation standards. Support AI-assisted QA and evaluation routines for generated code, calculation outputs, regression testing, and backtest validation. AWS, Data, and Platform Engineering Design and implement AWS-based platform components using services such as AWS Step Functions, Lambda, ECS/EKS, API Gateway, S3, CloudWatch, IAM, EventBridge, SQS/SNS, and AWS RDS. Build workflow orchestration for index calculations, backtest execution, data validation, exception handling, approvals, and operational monitoring. Integrate with data platforms including AWS RDS, cloud data platforms (such as Databricks, Snowflake, or Azure Synapse), data lakes, market data sources, reference data platforms, and analytical data pipelines. Ensure data lineage, audit trails, input/output traceability, logging, alerting, and operational controls are built into the platform. Support CI/CD, infrastructure automation, deployment validation, monitoring, and production support practices. Required Experience 12+ years of software engineering experience, with significant experience in Java full-stack development and enterprise platform delivery. 5+ years of technical leadership experience, including mentoring engineers, leading design discussions, reviewing code, and guiding delivery teams. Strong hands-on experience with Java, Spring Boot, REST APIs, microservices, relational databases, and backend service design. Hands-on frontend development experience using React, Angular, Vue, TypeScript, JavaScript, HTML, and CSS. Experience designing and delivering large-scale systems involving distributed services, workflow orchestration, data processing, APIs, and production operations. Experience with AWS cloud-native development, including compute, storage, orchestration, security, monitoring, logging, and managed databases. Strong understanding of automated testing, CI/CD, code quality, observability, secure development, and production readiness. Experience or strong interest in financial platforms, especially index calculation, options analytics, derivatives, equities, portfolio analytics, backtesting, risk systems, or capital markets technology. Ability to interpret detailed financial methodology specifications and translate them into reliable, testable, and auditable software designs. Exposure to GenAI engineering, AI-assisted coding, agentic development workflows, Claude Code, Claude Code CLI, Spec Kit, or Spec-Driven Development is highly desirable. Preferred Technical Stack Backend: Java, Spring Boot, REST APIs, microservices, JPA/Hibernate, Maven/Gradle, concurrency, batch processing, and enterprise integration patterns. Frontend: React, Angular, or Vue; TypeScript; JavaScript; HTML; CSS; reusable components; dashboards; forms; data grids; charts; and responsive UI design. Cloud: AWS Step Functions, Lambda, ECS/EKS, API Gateway, S3, CloudWatch, IAM, EventBridge, SQS/SNS, RDS, and cloud-native security patterns. Data Platforms: AWS RDS, cloud data platforms (such as Databricks, Snowflake, or Azure Synapse), relational databases, data pipelines, market data integration, reference data, and analytical data processing. AI Engineering: AI-assisted development, Spec-Driven Development, Claude Code, Claude Code CLI, Spec Kit, automated QA/evaluation routines, and human-reviewed generated code. DevOps and Quality: CI/CD, automated testing, integration testing, regression testing, performance testing, logging, monitoring, alerting, and production support. Financial Domain Knowledge The ideal candidate should have experience or strong working interest in financial systems involving calculation-heavy workflows. Relevant areas include: Index calculation methodologies, index levels, divisor logic, rebalancing, weighting, corporate actions, calendars, and daily calculation cycles. Options-based strategies such as covered call, put write, collar, volatility-based, delta-based, or rules-based options strategies. Backtesting concepts such as historical simulation, look-ahead bias prevention, survivorship bias, transaction assumptions, rebalance simulation, and reproducibility. Market data concepts including prices, option chains, strikes, expiries, implied volatility, rates, dividends, corporate actions, and reference data. Validation practices including golden datasets, tolerance checks, independent calculation verification, reconciliation, audit trails, and exception handling. Success Measures Leads the team in delivering a scalable, secure, and reliable index calculation and backtesting platform. Produces and guides high-quality Java full-stack implementation across backend services, frontend applications, APIs, workflows, and data integrations. Converts financial methodology specifications into tested, reproducible, and auditable platform logic. Establishes strong engineering practices for code quality, automated testing, observability, documentation, and production readiness. Uses AI-assisted development responsibly to improve delivery speed while maintaining human review, financial accuracy, and software quality. Builds strong collaboration across engineering, quantitative analysis, QA, infrastructure, product . click apply for full job details
This job is with S&P Global, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. About the Role: Grade Level (for internal use): 13 Role Summary We are seeking a Technical Team Lead - AI, AWS, Java Full-Stack, Financial Platforms to lead the design, development, and delivery of index calculation and back testing platform. This role combines hands-on Java full-stack engineering, AWS cloud development, AI-assisted software delivery, and technical leadership across a financial technology platform. The Technical Team Lead will guide a team of engineers in building scalable backend services, modern user interfaces, financial calculation workflows, back testing capabilities, data integrations, automated testing frameworks, and AI-assisted QA/evaluation routines. The role requires strong technical judgment, practical leadership, and the ability to translate financial methodology requirements into reliable, auditable, and production-ready software. This is a hands-on leadership role. The successful candidate will be expected to define technical direction, mentor developers, review architecture and code, collaborate with business and quantitative stakeholders, and contribute directly to critical platform components. Key Responsibilities Technical Leadership Lead the engineering delivery of an AI-enabled financial platform for index calculation, options analytics, back testing, and workflow execution. Define technical architecture, implementation standards, coding practices, testing expectations, and delivery patterns for the engineering team. Guide developers through complex design decisions involving Java services, frontend architecture, AWS workflows, data integration, AI-assisted development, and calculation accuracy. Partner with product owners, quantitative analysts, QA teams, infrastructure teams, data teams, and business stakeholders to convert requirements into clear technical plans. Lead design reviews, code reviews, sprint technical planning, production readiness reviews, and technical risk assessments. Mentor engineers in Java full-stack development, cloud-native design, financial calculation systems, automated testing, and responsible use of AI-assisted engineering tools. Ensure the platform is scalable, secure, maintainable, observable, auditable, and aligned with financial methodology and operational requirements. Hands-On Java Full-Stack Development Design and develop backend services using Java, Spring Boot, REST APIs, and enterprise application patterns. Build platform components for index calculation, backtesting, data processing, workflow orchestration, exception handling, validation, and reporting. Implement financial calculation logic based on methodology specifications, including options-based strategies, rebalancing rules, pricing inputs, market calendars, and historical backtesting assumptions. Develop modern frontend applications using React, Angular, Vue, TypeScript, JavaScript, HTML, and CSS. Build user interfaces for index setup, backtest configuration, workflow monitoring, calculation review, validation results, exception management, dashboards, and reporting. Ensure strong integration between frontend applications, backend APIs, authentication flows, data services, and cloud workflows. AI-Assisted Engineering and Spec-Driven Development Apply Spec-Driven Development practices to convert financial methodology documents, business requirements, and technical specifications into testable software components. Use AI-assisted engineering workflows to support planning, code generation, refactoring, test creation, documentation, and quality review. Review AI-generated or AI-assisted code for correctness, maintainability, security, performance, test coverage, and alignment with platform standards. Help establish team practices for responsible AI-assisted development, including review checklists, validation gates, test coverage expectations, and documentation standards. Support AI-assisted QA and evaluation routines for generated code, calculation outputs, regression testing, and backtest validation. AWS, Data, and Platform Engineering Design and implement AWS-based platform components using services such as AWS Step Functions, Lambda, ECS/EKS, API Gateway, S3, CloudWatch, IAM, EventBridge, SQS/SNS, and AWS RDS. Build workflow orchestration for index calculations, backtest execution, data validation, exception handling, approvals, and operational monitoring. Integrate with data platforms including AWS RDS, cloud data platforms (such as Databricks, Snowflake, or Azure Synapse), data lakes, market data sources, reference data platforms, and analytical data pipelines. Ensure data lineage, audit trails, input/output traceability, logging, alerting, and operational controls are built into the platform. Support CI/CD, infrastructure automation, deployment validation, monitoring, and production support practices. Required Experience 12+ years of software engineering experience, with significant experience in Java full-stack development and enterprise platform delivery. 5+ years of technical leadership experience, including mentoring engineers, leading design discussions, reviewing code, and guiding delivery teams. Strong hands-on experience with Java, Spring Boot, REST APIs, microservices, relational databases, and backend service design. Hands-on frontend development experience using React, Angular, Vue, TypeScript, JavaScript, HTML, and CSS. Experience designing and delivering large-scale systems involving distributed services, workflow orchestration, data processing, APIs, and production operations. Experience with AWS cloud-native development, including compute, storage, orchestration, security, monitoring, logging, and managed databases. Strong understanding of automated testing, CI/CD, code quality, observability, secure development, and production readiness. Experience or strong interest in financial platforms, especially index calculation, options analytics, derivatives, equities, portfolio analytics, backtesting, risk systems, or capital markets technology. Ability to interpret detailed financial methodology specifications and translate them into reliable, testable, and auditable software designs. Exposure to GenAI engineering, AI-assisted coding, agentic development workflows, Claude Code, Claude Code CLI, Spec Kit, or Spec-Driven Development is highly desirable. Preferred Technical Stack Backend: Java, Spring Boot, REST APIs, microservices, JPA/Hibernate, Maven/Gradle, concurrency, batch processing, and enterprise integration patterns. Frontend: React, Angular, or Vue; TypeScript; JavaScript; HTML; CSS; reusable components; dashboards; forms; data grids; charts; and responsive UI design. Cloud: AWS Step Functions, Lambda, ECS/EKS, API Gateway, S3, CloudWatch, IAM, EventBridge, SQS/SNS, RDS, and cloud-native security patterns. Data Platforms: AWS RDS, cloud data platforms (such as Databricks, Snowflake, or Azure Synapse), relational databases, data pipelines, market data integration, reference data, and analytical data processing. AI Engineering: AI-assisted development, Spec-Driven Development, Claude Code, Claude Code CLI, Spec Kit, automated QA/evaluation routines, and human-reviewed generated code. DevOps and Quality: CI/CD, automated testing, integration testing, regression testing, performance testing, logging, monitoring, alerting, and production support. Financial Domain Knowledge The ideal candidate should have experience or strong working interest in financial systems involving calculation-heavy workflows. Relevant areas include: Index calculation methodologies, index levels, divisor logic, rebalancing, weighting, corporate actions, calendars, and daily calculation cycles. Options-based strategies such as covered call, put write, collar, volatility-based, delta-based, or rules-based options strategies. Backtesting concepts such as historical simulation, look-ahead bias prevention, survivorship bias, transaction assumptions, rebalance simulation, and reproducibility. Market data concepts including prices, option chains, strikes, expiries, implied volatility, rates, dividends, corporate actions, and reference data. Validation practices including golden datasets, tolerance checks, independent calculation verification, reconciliation, audit trails, and exception handling. Success Measures Leads the team in delivering a scalable, secure, and reliable index calculation and backtesting platform. Produces and guides high-quality Java full-stack implementation across backend services, frontend applications, APIs, workflows, and data integrations. Converts financial methodology specifications into tested, reproducible, and auditable platform logic. Establishes strong engineering practices for code quality, automated testing, observability, documentation, and production readiness. Uses AI-assisted development responsibly to improve delivery speed while maintaining human review, financial accuracy, and software quality. Builds strong collaboration across engineering, quantitative analysis, QA, infrastructure, product . click apply for full job details
Spark Tek Inc
Alviso, California
Job Description Job Description Job Title: End to End Automation Test Manager Experience: 12-15 years Location: Santa Clara, CA Domain: Enterprise SaaS, CRM, Government Applications, Financial Systems Job Summary We are seeking an experienced Senior QA Lead / UAT Lead to drive end-to-end quality assurance, User Acceptance Testing (UAT), functional testing, integration testing, and release readiness for enterprise applications and cloud-based SaaS platforms. The ideal candidate will have extensive experience collaborating with business stakeholders, product owners, developers, and cross-functional teams to ensure that applications meet business requirements, functional expectations, and quality standards. This role requires strong leadership in UAT planning and execution, defect lifecycle management, requirements traceability, test strategy development, and stakeholder communication. The candidate should possess hands-on experience with test management tools, SQL-based backend validation, API testing, cloud applications, and enterprise integrations. Key Responsibilities 1. QA Strategy and Test Planning Develop and execute comprehensive QA strategies, test plans, test estimates, schedules, and resource plans. Lead functional, integration, regression, system, and user acceptance testing. Define testing scope, entry and exit criteria, acceptance criteria, and release readiness requirements. Identify testing risks, dependencies, and business-critical scenarios. Establish standardized QA processes, templates, metrics, and reporting practices. 2. User Acceptance Testing (UAT) Lead end-to-end UAT planning, preparation, execution, and sign-off activities. Collaborate with business users, product owners, and subject matter experts to understand business processes and requirements. Translate business requirements into UAT scenarios, test cases, and expected results. Maintain requirements-to-test traceability and ensure adequate test coverage. Coordinate UAT schedules, test data preparation, execution tracking, and business validation. Facilitate UAT review sessions and obtain stakeholder approval before production releases. 3. Defect Management and Resolution Lead daily defect triage meetings with business, development, product, and QA teams. Review defects for reproducibility, severity, business impact, and release priority. Ensure defects are documented with clear steps, test evidence, expected results, and actual results. Track defects through investigation, resolution, retesting, and closure. Communicate defect trends, outstanding risks, and quality metrics to project stakeholders. 4. SaaS Application and Integration Testing Lead testing of cloud-based SaaS applications, configurable workflows, business rules, and role-based access controls. Validate end-to-end business processes across integrated enterprise applications. Test REST APIs, web services, interface data exchanges, and cross-application dependencies. Validate data synchronization, batch processing, notifications, payment workflows, and reporting functionality. Perform backend data verification using SQL Server and Oracle databases. Support testing in AWS and other cloud-hosted application environments. 5. Release Management and Quality Reporting Coordinate smoke testing, regression testing, and release validation activities. Establish risk-based regression scope based on application changes and business impact. Assess overall application quality and communicate release risks to stakeholders. Prepare QA dashboards, test execution summaries, defect reports, and go/no-go recommendations. Ensure critical defects are resolved or appropriately assessed before production deployment. 6. Team Leadership and Stakeholder Collaboration Lead QA teams and coordinate testing activities across onshore, offshore, and globally distributed teams. Partner with business analysts, developers, product managers, project managers, and business users. Conduct requirements reviews, test case reviews, defect triage meetings, and status presentations. Mentor QA engineers and promote consistent testing practices. Identify opportunities to improve testing efficiency, coverage, and delivery quality. 7. Test Automation and Continuous Improvement Support test automation initiatives using Selenium, Python, and relevant automation frameworks. Collaborate with development and automation teams to maintain reliable regression suites. Identify repetitive test scenarios suitable for automation. Integrate automated testing into regression and release validation activities where appropriate. Improve test coverage, reduce manual effort, and strengthen overall QA effectiveness. Required Technical Skills Testing: Functional Testing, UAT, Integration Testing, Regression Testing, System Testing, Smoke Testing, Acceptance Testing. Test Management: Azure DevOps, Jira, Rally, Client Quality Center/ALM, Spira. Database: SQL Server, Oracle SQL, backend data validation, data integrity testing. API Testing: Postman, REST APIs, web services, interface validation. Automation: Selenium, Python, test automation support. Cloud and Enterprise Platforms: AWS, SaaS applications, Salesforce Service Cloud, Oracle Cloud HCM. Development and Technology Exposure: Java, .NET Core, JavaScript, enterprise application architectures. Delivery Methodologies: Agile, Scrum, Waterfall, SDLC, STLC, defect lifecycle management. Required Qualifications 12-15 years of experience in software quality assurance, software testing, and test leadership. Demonstrated experience leading UAT and coordinating with business stakeholders. Strong understanding of enterprise application workflows, business requirements, and acceptance criteria. Experience managing end-to-end testing across complex, integrated systems. Proficiency in test management tools, defect tracking, and quality reporting. Strong analytical, communication, leadership, and problem-solving skills. Experience managing multiple projects and collaborating with distributed delivery teams. Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field; a master's degree is an advantage.
Job Description Job Description Job Title: End to End Automation Test Manager Experience: 12-15 years Location: Santa Clara, CA Domain: Enterprise SaaS, CRM, Government Applications, Financial Systems Job Summary We are seeking an experienced Senior QA Lead / UAT Lead to drive end-to-end quality assurance, User Acceptance Testing (UAT), functional testing, integration testing, and release readiness for enterprise applications and cloud-based SaaS platforms. The ideal candidate will have extensive experience collaborating with business stakeholders, product owners, developers, and cross-functional teams to ensure that applications meet business requirements, functional expectations, and quality standards. This role requires strong leadership in UAT planning and execution, defect lifecycle management, requirements traceability, test strategy development, and stakeholder communication. The candidate should possess hands-on experience with test management tools, SQL-based backend validation, API testing, cloud applications, and enterprise integrations. Key Responsibilities 1. QA Strategy and Test Planning Develop and execute comprehensive QA strategies, test plans, test estimates, schedules, and resource plans. Lead functional, integration, regression, system, and user acceptance testing. Define testing scope, entry and exit criteria, acceptance criteria, and release readiness requirements. Identify testing risks, dependencies, and business-critical scenarios. Establish standardized QA processes, templates, metrics, and reporting practices. 2. User Acceptance Testing (UAT) Lead end-to-end UAT planning, preparation, execution, and sign-off activities. Collaborate with business users, product owners, and subject matter experts to understand business processes and requirements. Translate business requirements into UAT scenarios, test cases, and expected results. Maintain requirements-to-test traceability and ensure adequate test coverage. Coordinate UAT schedules, test data preparation, execution tracking, and business validation. Facilitate UAT review sessions and obtain stakeholder approval before production releases. 3. Defect Management and Resolution Lead daily defect triage meetings with business, development, product, and QA teams. Review defects for reproducibility, severity, business impact, and release priority. Ensure defects are documented with clear steps, test evidence, expected results, and actual results. Track defects through investigation, resolution, retesting, and closure. Communicate defect trends, outstanding risks, and quality metrics to project stakeholders. 4. SaaS Application and Integration Testing Lead testing of cloud-based SaaS applications, configurable workflows, business rules, and role-based access controls. Validate end-to-end business processes across integrated enterprise applications. Test REST APIs, web services, interface data exchanges, and cross-application dependencies. Validate data synchronization, batch processing, notifications, payment workflows, and reporting functionality. Perform backend data verification using SQL Server and Oracle databases. Support testing in AWS and other cloud-hosted application environments. 5. Release Management and Quality Reporting Coordinate smoke testing, regression testing, and release validation activities. Establish risk-based regression scope based on application changes and business impact. Assess overall application quality and communicate release risks to stakeholders. Prepare QA dashboards, test execution summaries, defect reports, and go/no-go recommendations. Ensure critical defects are resolved or appropriately assessed before production deployment. 6. Team Leadership and Stakeholder Collaboration Lead QA teams and coordinate testing activities across onshore, offshore, and globally distributed teams. Partner with business analysts, developers, product managers, project managers, and business users. Conduct requirements reviews, test case reviews, defect triage meetings, and status presentations. Mentor QA engineers and promote consistent testing practices. Identify opportunities to improve testing efficiency, coverage, and delivery quality. 7. Test Automation and Continuous Improvement Support test automation initiatives using Selenium, Python, and relevant automation frameworks. Collaborate with development and automation teams to maintain reliable regression suites. Identify repetitive test scenarios suitable for automation. Integrate automated testing into regression and release validation activities where appropriate. Improve test coverage, reduce manual effort, and strengthen overall QA effectiveness. Required Technical Skills Testing: Functional Testing, UAT, Integration Testing, Regression Testing, System Testing, Smoke Testing, Acceptance Testing. Test Management: Azure DevOps, Jira, Rally, Client Quality Center/ALM, Spira. Database: SQL Server, Oracle SQL, backend data validation, data integrity testing. API Testing: Postman, REST APIs, web services, interface validation. Automation: Selenium, Python, test automation support. Cloud and Enterprise Platforms: AWS, SaaS applications, Salesforce Service Cloud, Oracle Cloud HCM. Development and Technology Exposure: Java, .NET Core, JavaScript, enterprise application architectures. Delivery Methodologies: Agile, Scrum, Waterfall, SDLC, STLC, defect lifecycle management. Required Qualifications 12-15 years of experience in software quality assurance, software testing, and test leadership. Demonstrated experience leading UAT and coordinating with business stakeholders. Strong understanding of enterprise application workflows, business requirements, and acceptance criteria. Experience managing end-to-end testing across complex, integrated systems. Proficiency in test management tools, defect tracking, and quality reporting. Strong analytical, communication, leadership, and problem-solving skills. Experience managing multiple projects and collaborating with distributed delivery teams. Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field; a master's degree is an advantage.
