Sr Lead Software Engineer - UAT Data Platform & Governance Lead

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Sr Lead Software Engineer at JPMorgan Chase within Asset & Wealth Management, you will define and drive the firm-aligned strategy, operating model, and engineering execution for UAT data provisioning at scale. You will build and evolve a reusable UAT Data Platform that delivers fit-for-purpose, reliable UAT datasets across multiple Sources of Record (SORs) through standardized patterns, strong governance controls, and automated orchestration; improving environment stability and enabling repeatable testing outcomes. Job responsibilities Own the end-to-end platform and governance outcomes for UAT data refresh and provisioning. Set strategy and standards for UAT data provisioning, including reference architecture, reusable patterns, and “golden path” standards that teams can consistently adopt. Own the UAT Data Platform roadmap, prioritizing capabilities that increase dataset availability, reduce environment instability, and improve repeatability of test outcomes. Establish governance and operational controls for UAT data, including access management, auditability, retention, and quality/validation checks, partnering with risk and control stakeholders as needed. Lead end-to-end orchestration of UAT refreshes across multiple SORs, including dependency mapping, sequencing, cutover planning, runbook execution, and stakeholder communications. Build/extend self-service capabilities for refresh requests, automated workflow execution, standardized validation, and clear status/traceability for users and operators. Drive engineering excellence and reliability for the platform (secure coding, CI/CD, operational readiness, incident response discipline, postmortems, and continuous improvement). Influence across teams and functions to adopt standardized approaches, improve cross-system coordination, and apply modern engineering practices and tooling to UAT data operations. Act as the accountable technical leader for design and operational decisions impacting UAT data provisioning, platform scalability, and day-to-day stability. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability Demonstrated experience designing and building workflow-driven automation platforms (e.g., orchestration services, internal developer platforms, or data platforms). Strong programming skills in one or more languages such as Java, Python, or Go, with experience building APIs and distributed services. Experience with CI/CD, automation pipelines, and operational practices (runbooks, incident response, on-call readiness, postmortems). Proven ability to manage complex cross-system dependencies and execute coordinated releases/operational events across multiple stakeholders. Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Ability to tackle design and functionality problems independently with little to no oversight Practical cloud native experience Preferred qualifications, capabilities, and skills Experience with test data management and/or UAT environment refresh processes in large-scale, regulated environments. Familiarity with data protection patterns (masking/tokenization), privacy-by-design concepts, and controlled data access models. Experience with event-driven systems, scheduling/orchestration frameworks, and platform reliability engineering concepts. Exposure to modern cloud-native patterns (containers, infrastructure-as-code, service observability), and data ecosystem components. Platform Engineering: building reusable services, self-service workflows, paved paths, and golden patterns. Systems Thinking: understands data flows and failure modes across multiple SORs and downstream consumers.