Job Description
Overview Location: Charlotte, North Carolina; Stamford, Connecticut; Alpharetta, Georgia; Chicago, Illinois; New York, New York Salary: 135,000.00 - 230,000.00 USD Annual About Us Synchrony is more than a financial services company, we’re a team of passionate innovators committed to delivering best-in-class solutions that support millions of customers across the U.S. With a bold focus on technology, data, and digital innovation, we create meaningful experiences that simplify lives and enable financial wellness. When you join Synchrony, you become part of an inclusive culture where your voice matters, your growth is championed, and your work drives impactful results. Job Description Job ID 2601276 Category Technology Date posted 05/09/2026 Role Summary/Purpose: The VP, Senior Business Data Analyst - Cloud Data Migration will join the Data & Analytics Engineering (D&A) organization and lead the business analysis and stakeholder alignment needed to deliver a multi-year migration of Synchrony’s D&A data ecosystem to the cloud. This role is responsible for developing data-driven insights (e.g., data usage patterns, dependencies, and data-movement impacts) and translating them into a clear migration roadmap that protects the end-user experience, as well as managing that roadmap. The ideal candidate can design effective data models and analytical approaches to identify interdependencies across datasets, pipelines, and user workflows (i.e. what makes users “whole”), then convert those findings into practical recommendations that drive decisions and action. They will partner closely with stakeholders to define scope, timing, sequencing, as well as drive communications and end-user training needs for each migration bundle. They will collaborate with cloud migration leaders and workstream owners to ensure plans are aligned and executable. Success requires the ability to simplify complex technical topics for non-technical audiences into simple, easy-to-understand messages and to serve as a change agent who drives adoption while balancing program objectives with diverse business needs. Experience with data management and governance (including risk, protection and compliance considerations) is a plus. Essential Responsibilities: Evolve the enterprise data usage data model that captures end-to-end user patterns, lineage, and access/behavior signals across the D&A data ecosystem (e.g., database clusters, datasets, ETL/ELT pipelines, APIs). Continuously improve and make optimization recommendations on data model, data sources, definitions, and assumptions to increase transparency into consumption patterns, utilization, and power/cost drivers of data assets. Own the enterprise cloud data migration roadmap as the central point of coordination—driving alignment with business leaders, product/technology partners, and vendors on roadmap structure, timelines, dependencies, and prioritized sequencing of data asset migrations. Deliver regular, executive-ready updates to cross-functional stakeholders on progress, risks, issues, and decisions needed. Provide analysis and actionable insights to optimize migration execution, including: Manage existing migration sequencing that enables data analysts and downstream consumers to successfully transition to cloud-based data products Identification of PII and other sensitive data (at rest and in transit) within each migration wave, and coordination of required controls and mitigations Disposition recommendations (e.g., data model/structure improvements, code and data-movement efficiency, retirement of unused data assets, reduction of redundancy) Define success measures and establish metrics to track migration progress, adoption, risk reduction, and realized benefits; ensure metrics are consistently reported and actionable. Design and deliver automated reporting and dashboards (e.g., Tableau or equivalent) to provide near real-time visibility into status, milestones, risks, dependencies, and KPIs. Lead an agile team and develop talent (e.g., analysts) responsible for executing the migration roadmap and data optimization work; manage backlog, sprint planning, and delivery commitments. Establish a continuous improvement backlog capturing lessons learned, technical debt, and enhancement opportunities; partner with stakeholders to prioritize, plan, and execute improvements. Promote best practices across the data analytics commun