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# Senior Data Analyst - Fraud

- **Company:** Ally Financial Inc.
- **Location:** Charlotte, NC, United States (Remote available)
- **Experience:** Expert
- **Salary:** $85,000 - $150,000
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Application Programming Interfaces (APIs), Business Analytics Applications, Data Analysis, User Authentication, Big Data, Information Systems, Data Profiling, Database Queries, Python (Programming Language), Metadata, Operational Data Store, Power BI, Cloud Services, Data Streaming, Snowflake, Information Technology, Data Analytics, Performance Monitor, Data Management, Data Delivery
- **Published:** November 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f9fbfd4da54769f3

## About the Role

* Bachelor's degree in data analytics, information systems, computer science, statistics, mathematics, business, finance, or a related field, or equivalent work experience.
 * 3+ years of experience in data analysis, business intelligence, performance measurement, scorecard reporting, data management, financial services, fraud, risk, or a related discipline.
 * Strong SQL skills and experience querying, joining, validating, and analyzing large datasets across multiple sources.
 * Experience with data profiling, mapping, data-quality assessment, reconciliation, root-cause analysis, metadata, lineage, metric governance, and requirements documentation.
 * Experience developing and maintaining scorecards, dashboards, metric definitions, and visual analyses using Snowflake, Power BI, and Excel.
 * Working knowledge of data concepts such as real-time streaming, batch integration, APIs, data models, operational data stores, analytical platforms, and cloud-based services.
 * Strong analytical thinking, attention to detail, problem-solving, documentation, and verbal and written communication skills.
 * Ability to work independently, manage competing priorities, and collaborate effectively with business, product, data, and technology partners.
 * Experience with Python or another analytical programming language is preferred.
 * Experience with fraud, identity, authentication, payments, decisioning, investigations, or financial-crime data is preferred.

Minimum Qualifications:

 * 3+ years or relevant experience
 * Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics or equivalent

## Description

The Senior Data Analyst, Fraud Data & Value Stream Analytics will support two connected priorities: building the data foundation that powers fraud detection, decisioning, investigations, and customer protection, and developing Value Stream scorecards that measure business performance and outcomes. This role will partner with Fraud, Product, Data, Technology, Architecture, Analytics, Risk, Compliance, Finance, and Operations teams to translate business needs into clear data requirements, trusted metrics, and actionable insights.
The Analyst will map data across business processes and source systems, assess data quality and readiness, support real-time and batch data initiatives, and maintain documentation that improves lineage, traceability, and reuse. The role will also define and validate scorecard metrics, reconcile results to source data, produce recurring performance reporting, and explain trends and drivers to stakeholders. It is well suited for a curious, detail-oriented analyst who can connect business problems to data, communicate findings clearly, and work across teams to move complex initiatives forward., * Partner with business and product teams to understand fraud use cases, Value Stream objectives, business processes, decisions, and the data and measures required to support them.

 * Translate business needs into data requirements, source-to-target mappings, metric definitions, calculation rules, acceptance criteria, and analytical approaches.
 * Profile and analyze large, complex datasets to assess availability, completeness, accuracy, timeliness, consistency, and fitness for use.
 * Develop and maintain Value Stream scorecards, dashboards, metric dictionaries, calculation logic, targets, and recurring performance reporting.
 * Reconcile scorecard results to authoritative sources, investigate variances, document methodology and lineage, and clearly explain trends, drivers, limitations, and changes.
 * Maintain fraud data inventories, metadata, ownership, quality measures, lineage, and traceability across source and consuming systems.
 * Support real-time and batch data delivery through requirements validation, testing, reconciliation, production validation, and ongoing monitoring.
 * Communicate findings, risks, decisions, and recommendations to technical and nontechnical audiences while managing multiple priorities in a collaborative environment.