Senior Manager, Data Quality & Issue Management

Toyota North America•Plano, TX

22d

About The Position

Toyota Financial Services (TFS) Technology is seeking a highly motivated and strategic leader to serve as the Senior Manager, Data Quality & Issue Management. In this role, you will operate as a technology lead with deep expertise in Data Quality, Issue Management, and strong technical proficiency in data engineering. You will shape and drive the strategy, vision, and roadmap that elevate TFS’s enterprise data landscape and ensure the delivery of trusted, high‑value data across the organization. As the Senior Manager, Data Quality & Issue Management, you will define and execute the enterprise data quality vision and roadmap, embedding robust quality controls, maturing issue‑management processes, and advancing data transparency, usability, and governance. Through your technical leadership, you will guide teams in implementing scalable data‑quality solutions, enhancing data pipelines, and aligning quality standards with TFS’s enterprise data objectives. You will lead and develop a high‑performing team, fostering a culture grounded in accountability, continuous improvement, and cross‑functional collaboration. Your leadership will ensure that TFS’s data ecosystem is reliable, well‑governed, and fully equipped to support critical business decisions, meet regulatory expectations, and drive long‑term data product quality transformation across the enterprise.

Requirements

10 - 12+ years leading enterprise data‑quality programs, including strategy, controls, monitoring, and remediation across complex financial‑services environments
Proven experience managing the full issue lifecycle (identification, triage, RCA, remediation, closure) with strong familiarity with regulatory expectations in lending and credit reporting
Hands‑on experience supporting data needs across originations, customer servicing, collections, funding, remarketing, credit risk, and compliance within a captive or consumer‑lending organization
Experience partnering with data engineering teams to embed quality checks into pipelines, optimize data flows, and implement scalable data‑quality solutions
Practical experience implementing and operationalizing data cataloging, lineage, and data‑quality rules using Informatica CDGC and CDQ
Strong background in metadata management, stewardship workflows, and governance operating models using Collibra
Experience using Jira and Confluence to manage issue workflows, documentation, governance processes, and cross‑functional collaboration
Exposure to or hands‑on experience applying Agentic AI or MCP‑based automation to enhance data‑quality detection, anomaly identification, and issue‑remediation workflows
Experience supporting regulatory requirements such as credit reporting accuracy, fair lending, model governance, data privacy, and audit readiness
Deep understanding of governance frameworks, data ownership models, stewardship, and enterprise data‑management practices
Demonstrated success leading and developing high‑performing data teams, fostering accountability, continuous improvement, and cross‑functional alignment
Experience embedding quality standards into data products, ensuring reliability for analytics, reporting, and AI/ML use cases
Experience defining and tracking data‑quality KPIs, dashboards, and scorecards to measure performance and drive improvement

Nice To Haves

Hands-on experience with data governance tools such as Collibra and Informatica CDGC/CDQ
Proficiency in working with modern data engineering platforms, including Snowflake, Apache Iceberg, and Databricks
Strong understanding of data modeling concepts and best practices
Advanced expertise in writing, optimizing, and interpreting complex SQL queries
Experience in real-time data streaming platforms, including Apache Kafka, along with expertise in building and integrating RESTful microservices APIs
Deep understanding of data products, including lifecycle management and domain-driven design principles
Knowledge of Agentic architecture and Model Context Protocols (MCPs) to enable proactive data quality monitoring and data observability

Responsibilities

Define and execute the enterprise data‑quality strategy, roadmap, and operating model tailored to the needs of a captive auto‑finance business, ensuring trusted, accurate, and timely data across lending, servicing, collections, and risk functions.
Lead and develop two high‑performing teams, a combined group of 20 data‑quality analysts, engineers, stewards, and business partners, while fostering a culture of accountability, continuous improvement, and innovation.
Oversee the enterprise Issue Management program with strong executive presence and clear, influential communication to senior leadership. Set strategic direction, remove barriers, and elevate data‑quality standards enterprise‑wide.
Drive the enterprise Data Quality Issue Management program with strong executive presence, ensuring clear, concise, and influential communication with senior leadership.
Provide strategic direction, remove obstacles, and elevate data‑quality standards across the organization through disciplined execution, cross‑functional alignment, and proactive risk management.
Govern the end‑to‑end issue‑management process, including detection, triage, root‑cause analysis, remediation planning, and closure, ensuring alignment with regulatory expectations and internal controls.
Strengthen data governance practices by establishing standards, policies, and controls that improve data transparency, lineage, and accountability across business and technology teams.
Design and operationalize automated data‑quality rules, monitoring, and scorecards using tools such as Informatica CDGC/CDQ and Collibra ensuring consistent measurement of data accuracy, completeness, timeliness, and validity.
Collaborate closely with data engineering teams to embed quality checks into pipelines, optimize data flows, and ensure scalable, resilient data‑engineering solutions that support underwriting, funding, servicing, and collections.
Apply emerging Agentic AI and MCP‑based automation to enhance data‑quality detection, anomaly identification, issue triage, and remediation workflows, accelerating time‑to‑resolution and reducing manual effort.
Partner with business units (Originations, Servicing, Collections, Risk, Compliance, Finance) to understand data needs, resolve issues, and ensure alignment with enterprise data objectives and regulatory requirements.
Build, mentor, and develop a high‑performing team of data‑quality analysts, engineers, and stewards, fostering a culture of accountability, continuous improvement, and innovation.
Ensure data‑quality and issue‑management processes support regulatory compliance (e.g., fair lending, credit reporting accuracy, privacy, model governance) and withstand audit and examination scrutiny.
Partners with data product owners to embed quality standards into data products, ensuring reliability, usability, and trustworthiness across analytics, reporting, and AI/ML use cases.
Establish and track key data‑quality KPI’s, KRI metrics, dashboards, and scorecards to measure performance, identify trends, and drive continuous improvement.
Promote data literacy and stewardship across the organization, enabling business teams to better understand data definitions, lineage, and quality expectations.

Benefits

A work environment built on teamwork, flexibility, and respect
Professional growth and development programs to help advance your career, as well as tuition reimbursement
Team Member Vehicle Purchase Discount
Toyota Team Member Lease Vehicle Program (if applicable)
Comprehensive health care and wellness plans for your entire family
Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from Toyota regardless of whether you contribute
Paid holidays and paid time off
Referral services related to prenatal services, adoption, childcare, schools and more
Tax Advantage Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA
Relocation assistance (if applicable)