• Build, refactor, and maintain pipeline components of data products for a cloud environment • Own the ingestion, storage, cleansing, profiling, transformation, and presentation of data products • Analyze large and complex datasets for quality, accuracy, anomalies, and performance • Own pre-UAT testing for every change using the standard ABC framework • Consultatively interface with analysts and stakeholders on new data requests, enhancements, recommendations, quality, and awareness items • Assess project-specific integration restrictions/options and recommend load strategy, storage targets, data access layer model, and solution architecture • Participate in modeling star-schema data marts, denormalized performant layers, presentation layers, and Data Vault (or other immutable storage) • Organize source code with versioning framework • Create and maintain project-specific reference architecture diagrams and engineering-oriented documentation • Triage emergent data concerns and coordinate with stakeholders through resolution

• Engineer and validate consumer-level attributes from credit bureau and alternative data sources • Apply statistical techniques to uncover insights and improve predictiveness • Monitor attribute performance for stability, compliance, and accuracy • Build dashboards and visualizations (Tableau) to communicate findings • Collaborate with engineering to productionalize prototypes into scalable solutions • Partner with product, risk, and business teams to maximize impact across marketing, risk assessment, and collections • Engage with clients to explain analytics and translate technical results into applicable business insights

• Lead Experian's platform engineering modernization by unifying global engineering practices through automation and self‑service, not mandates. • Architect and scale an Automated Service Catalog that delivers standardized, lifecycle‑managed services via a frictionless, "path of least resistance" experience for engineers across LATAM, North America, UK&I, and EMAP. • Convert a conceptual global vision into a clear strategy, roadmap, operating model, and governance adopted across regions. • Design and launch a one‑click self‑service catalog for standardized, API‑driven services. • Apply AI/agentic automation to decisioning to convert days/weeks into minutes. • Establish a federated contribution model for developers globally to publish, validate, and reuse automations. • Partner with Security, Cloud, Engineering, Architecture, Data, and Product to simplify architectures and unify standards that can be automated. • Build continuous feedback loops with engineering teams; iterate for measurable outcomes.

• The Lead Azure Data Engineer is responsible for migration, governance, and data integration for the implementation of new Microsoft Cloud based document routing and task management system. • They will ensure alignment with the technical, security, and document management requirements such as leading data discovery and mapping, validating metadata standards, managing data migration into Dataverse and SharePoint Online, and ensuring proper implementation of retention, auditability, and role-based access controls within the Microsoft SaaS architecture.