Principal Enterprise Data Quality Analyst

About this role

Who We Are Join a team that puts its People First! Since 1889, First American (NYSE: FAF) has held an unwavering belief in its people. They are passionate about what they do, and we are equally passionate about fostering an environment where all feel welcome, supported, and empowered to be innovative and reach their full potential. Our inclusive, people-first culture has earned our company numerous accolades, including being named to the Fortune 100 Best Companies to Work For® list for eleven consecutive years. We have also earned awards as a best place to work for women, diversity and LGBTQ+ employees, and have been included on more than 50 regional best places to work lists. First American will always strive to be a great place to work, for all. For more information, please visit www.careers.firstam.com.

What We Do We are seeking a Principal Enterprise Data Quality Analyst to help build, operationalize, and mature the enterprise data quality program across priority data domains, systems, reports, pipelines, and Critical Data Elements (CDEs). This role makes data quality measurable, transparent, and governable by defining frameworks, rules, metrics, scorecards, monitoring routines, issue workflows, and evidence practices.

You will partner with technical teams, business stakeholders, Risk, Audit, Information Security, and governance partners to improve trust in critical enterprise data, reporting, decision-making, and control readiness. The role defines, monitors, enables, coordinates, and escalates; while accountable business and technology teams remain responsible for source-system correction, operational cleansing, and remediation execution.

This is an individual contributor role for a hands-on practitioner who can turn standards into repeatable routines, communicate technical issues in business terms, influence without direct authority, and bring structure to complex data quality challenges.

WHAT YOU'LL DO Build the data quality operating model · Define and maintain data quality dimensions, rule design standards, scoring methodology, control expectations, procedures, playbooks, templates, and adoption routines. · Partner with Data Owners and Data Stewards to identify CDEs, authoritative sources, business definitions, quality expectations, thresholds, monitoring needs, and accountability based on business risk and operational impact. · Establish and manage the Data Quality Rule (DQR) lifecycle, including intake, definition, approval, testing, implementation, change-triggered revalidation, periodic review, and linkage to stewardship accountability. · Align quality rules and standards to glossary terms, metadata, catalog records, lineage context, and governance policies so expectations are traceable, consistent, and auditable. Define, measure, and monitor data quality · Profile, validate, reconcile, and analyze data across systems, integrations, pipelines, reports, and downstream consumption to identify defects, anomalies, patterns, trends, and improvement opportunities. · Translate business expectations into measurable rules, dimensions, thresholds, controls, acceptance criteria, KPIs, KRIs, dashboards, scorecards, and exception reporting. · Monitor quality results, threshold breaches, rule coverage, issue aging, ownership gaps, and stewardship progress; communicate implications clearly to business and technical stakeholders. · Support trusted-data practices that distinguish compliant data from non-compliant or at-risk data. Manage issues and coordinate sustainable remediation · Operate a governed issue process, including intake, assessment, impact analysis, triage, prioritization, escalation, status reporting, and resolution tracking. · Support root-cause analysis with Data Owners, Stewards, Custodians, architects, engineers, application teams, BI teams, and other domain partners. · Coordinate remediation planning, validate retesting results, document outcomes, and distinguish tactical fixes from systemic improvements that prevent recurrence. · Escalate recurring defects, control weaknesses, systemic themes, or ownership gaps to the appropriate governance or oversight forum. Embed quality into delivery and adoption · Incorporate data quality requirements into projects, system changes, reporting initiatives, data products, integrations, pipeline design, schema changes, and release management. · Partner with engineering, integration, architecture, platform, and application teams to define quality gates, validation checkpoints, monitoring requirements, and CDE or DQR impact assessments. · Ensure rules, results, issues, ownership, lineage, and remediation evidence are documented in appropriate governance, metadata, catalog, workflow, or reporting tools. · Prepare governance, leadership, risk, audit, and executive-ready reporting on quality performance, trends, rule coverage, issue status, ownership gaps, control effectiveness, business impact, and recommended actions.

WHAT YOU'LL BRING · Bachelor's degree in information systems, data analytics, computer science, business, finance, mathematics, statistics, engineering, or a related field, or equivalent practical experience. · 5+ years of experience in data quality, data governance, enterprise data management, analytics, BI, data engineering, business analysis, metadata, stewardship, analytics controls, or related data-focused roles. · 3+ years of hands-on experience defining, measuring, monitoring, profiling, validating, reconciling, or improving data quality in complex enterprise environments. · Strong SQL skills and experience investigating data issues across source systems, transformations, integrations, pipelines, reports, and downstream consumption. · Strong understanding of CDEs, DQRs, profiling, validation, completeness, accuracy, consistency, timeliness, uniqueness, thresholding, controls, exception handling, issue management, remediation, and root-cause analysis. · Experience translating business requirements into measurable

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