• Conduct client data readiness assessments, evaluating source systems, data quality, and migration complexity during early implementation phases. • Define data cleansing, transformation, and validation frameworks to improve migration success rates. • Partner with Implementation Project Managers and Solution Consultants to ensure data architecture supports predictable delivery timelines. • Establish standardized migration playbooks, mapping templates, and reusable ETL frameworks to accelerate onboarding and reduce implementation risk. • Design and maintain conceptual, logical, and physical data models within Microsoft Dynamics 365 (Protech). • Define and document data architecture standards, integration patterns, and governance best practices. • Architect and oversee ETL processes using SSIS and other standard ETL tools. • Design and implement integrations leveraging KingswaySoft and/or Pentaho Data Integration. • Lead data migration strategy and execution support for onboarding and implementation projects. • Identify data dependencies, risks, and integration gaps early in the delivery lifecycle. • Ensure secure, scalable, and high-performance data exchange between D365 and external systems. • Collaborate with Product and Engineering to assess the architectural impact of platform enhancements. • Partner with Analytics teams to ensure reporting accuracy, consistency, and data integrity. • Develop reusable integration templates, migration accelerators, and standardized data patterns. • Proactively identify systemic data design issues and recommend improvements to reduce rework and onboarding delays. • Utilize AI-enabled tools to analyze source system data structures, identify data quality risks, and support scalable migration and integration architectures. • Maintain clear documentation of data flows, mappings, transformations, and integration logic.

Role Description The Fraud Analytics & Commercialization team drives Experian's fraud analytics business through four integrated functions: pre-sales engagement, scalable and custom solutions, consulting, and operational enablement, with the goal of becoming the industry's provider-of-choice. We're looking for a hands-on Data Modeling Expert to join our Fraud Analytics Consulting team as a founding member of the first fraud analytics consortium purpose-built for the TelCo industry. The ideal candidate brings investigative curiosity, an impact-focused mindset, and the ability to translate ambiguous client questions into clear analytical plans executed with confidence, and thrives in unstructured environments where the path forward isn't predefined. In this role, you'll have the opportunity to: - Work directly with TelCo clients to conduct investigative analytics, identify emerging fraud trends, and develop insights that only cross-industry consortium data can reveal. - Co-build custom fraud detection models with client teams, guiding them through Experian's proprietary data, analytics environment, and fraud detection methodologies. - Train and onboard clients on the platform, helping them build familiarity and confidence with Experian's analytics environment. - Translate ambiguous client questions and needs into well-defined hypotheses and analytical plans, then execute them independently and communicate findings clearly. - Define and implement a repeatable, scalable proof-of-value process that demonstrates the consortium's value to prospective and existing clients. - Build reusable tools, templates, and analytical frameworks that enable consistent, efficient delivery as the consortium grows. - Build confidence and credibility with client analysts, client executives, and internal Experian product and sales partners. - Collaborate with Experian's product team as an expert user, providing direct input to influence the development roadmap. Qualifications - 6+ years of experience in data science, analytics, or a related field. - Degree (undergraduate or graduate) in Statistics, Econometrics, Electrical Engineering, or another quantitative discipline, or an equivalent combination of education and experience that shows quantitative reasoning and analytical ability. - Deep expertise in analytics and machine learning, with hands-on experience across the full modeling lifecycle; preference for experience in fraud, credit, or TelCo analytics. - Investigative analytics mindset - experienced at identifying patterns, forming hypotheses, and drawing meaningful conclusions from large-scale datasets. - Demonstrated capability to operate independently, translate ambiguous client needs into well-defined hypotheses, and execute clear analytical plans from start to finish. - Experience working directly with clients or business partners in a consulting or advisory role, building confidence and credibility across technical and executive audiences. - Experience designing or contributing to scalable analytical processes, tools, or frameworks. - Strong Python skills for data analysis and machine learning, including PySpark, Polars, NumPy, and Pandas; familiarity with large-scale data processing frameworks and cloud platforms, especially Spark and AWS. - Willingness to travel periodically for on-site client workshops and engagements. Benefits - Great compensation package and bonus plan. - Core benefits including medical, dental, vision, and matching 401K. - Flexible work environment, ability to work remote, hybrid or in-office. - Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.

• Build and support data pipelines that can handle the volume and complexity of data while ensuring scale, data accuracy, availability, observability, security, and optimum performance • Developing and maintaining data warehouse tables, views, and models, for consumption by analysts and downstream applications • Own and lead workstreams while driving results through self or your team • Participate in periodic data engineering activities, e.g. monthly insights reporting, profile data updates, etc • Troubleshoot production issues, and participating in on-call activities • Identify several areas for improving data engineering processes, and share with the team • Contribute consistently towards building our data platform, which includes data pipelines, and data warehouse layers • Independently own and lead workstreams whether it is periodic data engineering activities, or work items in support of our roadmap • Deepen your understanding, and build subject matter expertise of our data & ecosystem • Your contributions have led to us making significant progress in implementing the data platform strategy, and key data initiatives to support the company's growth • You’ve established yourself as a key team member with subject matter expertise within data engineering

• Design and enforce Bronze / Silver / Gold architecture and layering principles • Lead global data model harmonization (conformed dimensions, unified facts, SCD2) • Drive migration from Teradata to BigQuery with parallel validation and cutover strategy • Design end-to-end pipelines (ingestion → transformation → consumption) using Airflow • Establish data governance framework: lineage, metadata, RBAC, auditability • Implement data quality & observability with automated checks and pipeline gating • Define CI/CD processes and schema evolution strategy (Flyway, Git-based workflows) • Optimize performance and cost (partitioning, clustering, aggregation strategies) • Align architecture with finance, tax, and compliance requirements