Principal Enterprise Data Quality Analyst

First American•Santa Ana, CA

7d•$129,300 - $172,300

About The Position

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.

Requirements

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 data quality rules, metrics, thresholds, dashboards, scorecards, monitoring routines, or controls.
Working knowledge of data governance, stewardship, ownership, metadata, business glossary, catalog, lineage, policy, change management, auditability, and control expectations.
Strong facilitation, communication, documentation, stakeholder management, and influencing skills in federated, matrixed environments.

Nice To Haves

Experience helping build, launch, or mature an enterprise data quality, governance, stewardship, or data management capability.
Experience in financial services, lending, mortgage, servicing, risk, finance, customer data, healthcare, insurance, or another regulated or highly controlled data domain.
Experience with tools such as Informatica, Collibra, Microsoft Purview, Unity Catalog, Tableau, Power BI, Jira, ServiceNow, or similar data quality, governance, metadata, BI, workflow, or issue-management platforms.
Experience with data warehouses, data lakes, lakehouse platforms, ETL/ELT pipelines, cloud data platforms, APIs, integrations, and enterprise reporting environments.
Familiarity with DAMA-DMBOK, DCAM, CDMP, data stewardship models, CDE frameworks, data product practices, or data governance operating models

Responsibilities

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.
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.
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.
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.