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.