Software Engineering Technical Team Lead

spgi; Princeton (NJ); On-site; USD 150,000 - 190,000; Full time

Job summary

SPGI is 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 hands-on leadership role guides engineers in Java backend services, modern UIs, AI-assisted development, and data workflows, translating financial methodologies into production software.

You will mentor engineers, review architecture, and collaborate with product owners and quantitative teams to ensure secure,

Qualifications

• 12+ years of software engineering experience.
• Significant Java full‑stack development experience.
• Enterprise platform development experience.

Responsibilities

• Lead 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.
• 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.
• 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.
• 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.

Skills

Java full-stack

AWS

AI-assisted engineering

Technical leadership

REST APIs

Frontend development

Tools

Spring Boot

AWS Lambda

API Gateway

S3

RDS

Databricks

Snowflake

Job description

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

• 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 platf