Tech Lead, Post-Trade Platform (Java/Angular)

What You'll Do
Own the end-to-end design and delivery of a defined functional area within the post-trade platform, from requirements through production support.
Translate post-trade requirements from Operations, Middle Office, and Finance stakeholders into clear technical designs, delivery plans, and well-scoped work.
Lead, coach, and review the work of a small development team while setting standards for code quality, testing, observability, and documentation.
Design and build event-driven Java services using Spring Boot, messaging, and REST APIs, and contribute to Angular front-end development for operations users.
Make and document foundational architecture decisions for a greenfield, cloud-native platform running on Kubernetes in AWS.
Identify and deliver AI-enabled automation for operational processes, including exception triage, break investigation, reconciliation matching, and processing unstructured confirmations, with appropriate controls and human oversight.
Embed effective AI-assisted development practices and establish guardrails for reviewing, testing, and maintaining AI-generated code.
Partner with architecture, data, infrastructure, security, control functions, and global colleagues to deliver reliable platform capabilities.

What You Bring
Hands-on experience building financial-services systems across the post-trade lifecycle, including trade capture and enrichment, allocation, confirmation and affirmation, clearing, settlement, reconciliation, lifecycle events, and exception management.
Strong understanding of the trade, position, cash, and reference data that support post-trade workflows, along with the associated operational controls.
Lead-level Java expertise, including Java 17 or later, Spring, Spring Boot, microservice design, event-driven architectures, messaging platforms such as Kafka, RESTful APIs, relational databases, and SQL.
Working experience with Angular and TypeScript, with the ability to build, review, and guide front-end development.
Strong engineering discipline across automated testing, CI/CD, code review, observability, resilience, auditability, and data integrity.
Practical, critical use of AI coding assistants and an understanding of LLM-based techniquesincluding retrieval-augmented generation, agents, document extraction, and classificationfor post-trade and Middle Office use cases.
Awareness of AI-related accuracy, explainability, auditability, data-privacy, and human-review requirements.
Proven experience leading a small team to deliver against agreed scope and timelines, with strong communication skills, sound judgment, and comfort operating in a greenfield environment.