Jobs at Bread Financial

Develop and maintain full-stack financial technology applications across frontend, backend, and UI/UX layers. Responsibilities include writing clean Golang and other software code, unit testing, peer reviews, troubleshooting, incident support, Agile collaboration, cloud development, and continuous application improvements. The role requires participation throughout the SDLC and collaboration with cross-functional teams to deliver scalable, reliable solutions.

Leads manual and complex functional testing across the software development lifecycle. Designs test plans and scenarios, validates data flows using advanced SQL and DB2, investigates defects, performs production and first-event validation, and reports test progress and defect trends. Mentors junior testers, reviews requirements for testability, collaborates with developers and project leads, supports integration activities, and improves documentation and reporting accuracy.

Supports financial applications, troubleshoots system issues, assists with upgrades and implementations, and analyzes financial and operational data. The role develops reports, validates data, documents requirements and workflows, supports process improvements and automation, and collaborates with finance, technology, and business teams on system enhancements. It also contributes to financial modeling, forecasting, reporting accuracy, and business decision-making.

Oversees major incident response and problem root-cause analysis, monitors ITIL/ITSM processes and service-level metrics, reports trends, coordinates change management, facilitates stakeholder communications, mentors analysts, develops procedures and training, and drives continuous improvement across incident, problem, availability, and change management practices.

Defines enterprise quality engineering strategy, leads automation architecture and testing modernization, establishes quality standards and governance, advises senior leadership on quality risks and investments, advances testing methodologies across web, API, and data layers, and mentors senior engineering teams.

Leads enterprise data governance initiatives covering data quality, metadata management, critical data elements, cataloging, lineage, profiling, and monitoring. Develops and executes data management roadmaps, strengthens regulatory data controls, partners with business, risk, legal, and technology teams, and manages a distributed U.S. and India team. Oversees governance platforms, stakeholder engagement, training, reporting, and continuous improvement in a highly regulated banking environment.

Leads AI governance and risk oversight by assessing AI systems, tools, applications, and third-party solutions. Evaluates testing, monitoring, controls, explainability, reliability, bias, drift, hallucinations, and human oversight. Develops governance policies, standards, approval processes, metrics, and executive reporting. Coordinates incident response, remediation, audit readiness, and cross-functional risk management across technology, cybersecurity, legal, privacy, compliance, data governance, and enterprise risk teams.

Leads global IT audits and risk assessments covering information systems, security, privacy, and control frameworks. Evaluates complex technology controls, identifies risks, reports findings, and recommends corrective actions. Manages audit scopes, timelines, budgets, staff development, annual risk planning, and coordination with SOX testing. Partners with senior technology, security, risk, and business leaders while overseeing a team of 2–5 direct reports.

Leads the architecture, development, and evolution of cloud-native platform services, shared APIs, SDKs, automation, and developer self-service capabilities. Establishes standards for reliability, security, CI/CD, governance, observability, and scalable platform adoption. Drives AI-enabled engineering platforms, including LLMs, agents, MCP, and RAG, while providing technical leadership, mentoring, and cross-functional collaboration across engineering, product, security, architecture, and operations teams.

Implement and optimize AI Operations and observability capabilities for event correlation, anomaly detection, alert-noise reduction, incident response automation, and operational analytics. Configure monitoring and ITSM integrations, develop automated remediation workflows, evaluate AI/ML operational use cases, and support SRE and service-management teams. The role also provides technical guidance, documents best practices, and assesses emerging AIOps technologies and platform enhancements.