Software Engineering Technical Team Lead (Princeton, NJ)

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):

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.

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