Staff Business Analyst (Employer of Record)
Credit Acceptance
- Location
- India - Remote
- Work model
- Remote
- Level
- Staff
- Posted
- Aug 13, 2026
About this role
Credit Acceptance is proud to be an award-winning company with workplace recognition in multiple categories! Our world-class culture is shaped by dedicated Team Members who share a drive to succeed as professionals and together as a company. A great product, amazing people and our stable financial history have made us one of the largest used car finance companies in the United States. In this role, you will work as a dedicated member of a globally distributed team, partnering closely with business partners in the U.S. to design, build, and scale solutions that directly impact our customers and operations. While your legal employer will be our EoR partner, you will be fully integrated into our Credit Acceptance team for day-to-day work and collaboration. As a Staff Business Analyst with a Data Engineering focus, you operate with a strategic, solution-driven mindset, aligning enterprise data initiatives to business objectives and long-term data strategy. You serve as a subject matter expert across multiple domains, connecting business processes, data requirements, and technical implementation at an enterprise scale. You lead the definition of data standards, including traceability, lineage, and quality, ensuring data assets are scalable, governed, and usable across the organization. You drive advanced analytics and insight generation that informs executive decision-making, while championing a culture where all data initiatives are measured against business value realization. You partner with Product, PMO, and Engineering to ensure sustainable data quality, enable self-service analytics, and deliver data solutions that support evolving enterprise goals. This position will work remotely from India. Outcomes and Activities: Drives Data Strategy, Governance, and Standards Leads with a strategic, solution-driven mindset, ensuring data initiatives align to enterprise goals and long-term data strategy. Defines and drives enterprise standards for traceability, lineage, data mapping, and data quality, embedding them into scalable and sustainable architectures. Leads the design and adoption of governance frameworks that ensure data usability, compliance, and long-term value across the organization. Connects Business Needs to Technical Execution Partners with PMO, Product, stakeholders, architects, and engineering teams to translate business needs into scalable, future-proof data solutions. Demonstrates deep expertise across domains by connecting business processes, data requirements, and system design to drive cross-functional strategy. Ensures business logic is accurately implemented in technical solutions and aligned across end-to-end data ecosystems. Leads Strategic Data Analysis and Insight Generation Leads complex, cross-domain data analysis initiatives to uncover insights, validate data integrity, and support decision-making. Sets the standard for business insight generation through advanced analytics that influence executive decisions. Applies strong understanding of application data and design to recommend efficient, practical, and value-driven solutions. Manages Delivery, Risk, and Execution Quality Maintains and refines application backlogs, ensuring clarity, prioritization, and alignment to business objectives. Identifies and manages risks related to scope, timeline, budget, and business impact, recommending mitigation strategies. Ensures application performance, reliability, and stability by partnering closely with engineering teams. Applies and improves standards for coding, documentation, testing, and delivery processes. Establishes Data Quality, Process Improvement, and Operational Excellence Identifies data and process gaps across systems and domains, driving cross-functional resolution and continuous improvement. Leads initiatives to enhance data quality, system performance, and operational scalability. Decreases incidents, improves system uptime, and strengthens overall data reliability. Continuously evaluates and