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Data Governance Quality Analyst Jobs (NOW HIRING)

Morgan Stanley

Analyst, Data Governance & Quality

Plano, TX · On-site

$80K - $100K/yr

The Data Governance & Quality Analyst supports data lineage and governance activities by helping maintain data lineage documentation, collecting information from stakeholders, and assisting with data ...

Morgan Stanley

Analyst, Data Governance & Quality

Allen, TX · On-site

$80K - $100K/yr

The Data Governance & Quality Analyst supports data lineage and governance activities by helping maintain data lineage documentation, collecting information from stakeholders, and assisting with data ...

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How much do data governance quality analyst jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for data governance quality analyst in the United States is $54.78, according to ZipRecruiter salary data. Most workers in this role earn between $40.62 and $67.07 per hour, depending on experience, location, and employer.

What does a data governance quality analyst do?

A Data Governance Quality Analyst is responsible for ensuring the accuracy, consistency, and security of data within an organization. They establish data quality standards, monitor compliance with data governance policies, and identify areas where data processes can be improved. Their work involves collaborating with various departments to implement data management best practices and resolve data-related issues. Ultimately, they help organizations make informed decisions by maintaining high-quality, reliable data.

What are the key skills and qualifications needed to thrive as a data governance quality analyst, and why are they important?

To thrive as a Data Governance Quality Analyst, you need a solid understanding of data management principles, data quality frameworks, and familiarity with data governance policies, typically supported by a degree in information systems, computer science, or a related field. Proficiency with data quality tools (such as Informatica Data Quality, Collibra, or Talend), data profiling, and experience with SQL or other database systems are essential. Attention to detail, analytical thinking, and strong communication skills help analysts collaborate across departments and identify data quality issues effectively. These skills ensure the integrity, consistency, and compliance of organizational data, supporting reliable business decision-making and regulatory requirements.

How does a data governance quality analyst typically collaborate with other departments to ensure data quality standards are met?

A Data Governance Quality Analyst works closely with teams across the organization, such as IT, business units, and compliance, to establish and maintain data quality standards. They facilitate regular meetings to review data processes, address discrepancies, and implement corrective actions when issues arise. Effective communication and documentation are essential, as the analyst must translate technical data requirements into actionable guidelines for non-technical stakeholders. This collaborative approach helps ensure that all departments consistently adhere to data governance policies, leading to improved data reliability and regulatory compliance.

What is the difference between Data Governance Quality Analyst vs Data Analyst?

AspectData Governance Quality AnalystData Analyst
Required SkillsData quality management, governance policies, complianceData analysis, visualization, reporting
Work EnvironmentCorporate, compliance-focused teamsBusiness units, analytics teams
CertificationsData governance, data quality certificationsData analysis, BI tools certifications

The Data Governance Quality Analyst primarily focuses on ensuring data quality, compliance, and governance policies within organizations. In contrast, a Data Analyst concentrates on analyzing data to generate insights and support decision-making. While both roles require strong data skills, the Governance Quality Analyst emphasizes data standards and policies, whereas the Data Analyst emphasizes data interpretation and visualization.

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Data Governance Quality Analyst jobs near you
Infographic showing various Data Governance Quality Analyst job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $113,939 per year, or $54.8 per hour.

Data Governance & Quality Analyst

Arctiq, Inc.

Philadelphia, PA • On-site

Contractor

Posted 6 days ago

Key responsibilities

  • Monitor and embed data quality controls across the full data lifecycle, including ingestion, transformation, and consumption.

  • Define, implement, and maintain data quality rules, SLAs, and validation checks in collaboration with data engineers and business stakeholders.

  • Develop and validate data quality scorecards, KPI dashboards, and perform reconciliation audits to ensure reporting integrity.

Job description

Company Overview:
Arctiq is a global, intelligence-driven technology services company delivering professional and managed services across Hybrid Cloud Infrastructure, Networking & Connected Experiences, Cybersecurity, Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help organizations operate, secure, and modernize complex environments by unifying infrastructure, networking, data, security, automation, and observability under a single, integrated operating model. Our work focuses on helping customers reduce operational friction, improve resilience, and make better, faster decisions as their environments evolve. Arctiq builds on decades of industry expertise and a customer-centric ethos to deliver exceptional value to clients across diverse industries.
This is a contract-to-hire opportunity with one of Arctiq's clients. It's a remote position working EST hours.
Position Overview:
We are seeking a Data Governance & Quality Analyst to monitor and embed data quality controls across the full data lifecycle from raw ingestion through bronze, silver, and gold medallion layers to consumption in reports, dashboards, and automated business processes.
You will work with business stakeholders to define quality rules, SLA thresholds, monitor, manage master data, maintain metadata and lineage catalogs, and partner with data engineers and business stakeholders to ensure every dataset is accurate, complete, timely, and trustworthy
Responsibilities:
Data Quality - Ingestion & Pipeline Layer
  • Design and work with data engineers to implement data quality validation checks within Azure Data Factory and Microsoft Fabric ingestion pipelines, covering completeness, accuracy, consistency, uniqueness, and timeliness dimensions.
  • Define critical-field SLAs (e.g., null-rate thresholds, duplicate tolerances, freshness windows) for each data source and account for data-dictionary revisions.
  • Provide feedback to data engineers to fine tune automated stop / alert / continue actions ensuring bad data does not propagate downstream.
  • Ensure data engineers implement escalation-aware alerting tied to business-critical deadlines where standard alert cadences must accelerate to prevent missed processing windows.
  • Support onboarding of new data feeds within a target 45-day window, including definition of quality rules and acceptance criteria for each new source.

Data Quality - Transformation & Gold Layer
  • Author and maintain data quality rules applied during medallion-architecture transformations (bronze → silver → gold), including cross-source reconciliation checks for journal entry automation (Accrued Wages, Ending Inventory, Sales Recap, Transfers, etc.).
  • Validate standardized formats and account mappings after transformation; flag and escalate anomalies (e.g., missing GL mappings, orphaned store records, mismatched payroll-GL identifiers).
  • Implement automated duplicate detection and anomaly alerting when null patterns, duplicates, or statistical outliers exceed governance-defined thresholds.
  • Support field-level encryption validation for PII elements (SSN, full DOB, ZIP) and verify row-level security (RLS) enforcement at the data layer.

Data Quality - Direct-to-Gold Feeds
  • Validate data feeds that bypass the bronze and silver layers and arrive directly at the gold layer including payroll and invoice submissions by implementing gold-layer handshake checks that confirm what was sent matches what was received.
  • Work with business stakeholders to define reconciliation and audit-trail rules for direct-to-gold feeds to ensure reporting integrity, recognizing that these datasets represent immutable historical records (e.g., exact payroll disbursements).

Data Quality - Consumption & Reporting Layer
  • Develop, maintain and validate data quality scorecards and KPI dashboards in Microsoft Purview and / or Power BI, reporting on dimensions such as completeness, accuracy, timeliness, and conformity across all governed datasets.
  • Perform periodic reconciliation audits comparing platform outputs against source-of-record systems to confirm reporting integrity.
  • Report Release Validation Gate - Establish a mandatory validation checkpoint for all new Power BI reports and any existing reports undergoing rework before they are promoted to production. This gate will leverage AI-driven profiling of the underlying semantic model to generate an automated summary of central tendency, dispersion, and distributional shape for each dataset, ensuring that data patterns and outliers are surfaced and reviewed prior to end-user consumption.
  • Business Glossary Alignment for Reporting Attributes - Coordinate with Business Analysts to ensure that every new reporting attribute introduced through a Power BI report has a corresponding business glossary term defined and published in Microsoft Purview before the report goes live. For existing attributes in reworked reports, initiate a review cycle with the designated business owner to confirm that current glossary definitions remain accurate and aligned with evolving business usage.

Master Data Management (Profisee)
  • Administer and govern master data entities in Profisee, including master client records and store-level unique identifiers.
  • Define and enforce matching, merging, and survivorship rules to maintain a single golden record for each entity.
  • Collaborate with business analysts to onboard new entity types and attributes.
  • Ensure all mastered identifiers propagate correctly through the medallion layers and downstream applications.

Metadata, Lineage & Cataloging (Microsoft Purview)
  • Maintain the enterprise data catalog in Microsoft Purview, including data dictionary entries, business glossary terms, data classification labels, and sensitivity tags.
  • Document and publish end-to-end data lineage from source systems through Lakehouse transformations to Power BI reports and automated business processes.
  • Define and apply data classification and sensitivity labels aligned with PII and PCI compliance requirements.
  • Track and publish change history for business-defined critical fields; ensure metadata stays current as pipelines and schemas evolve.

Governance Framework & Collaboration
  • Contribute to the data governance framework for onboarding external data providers, including quality acceptance criteria, SLA definitions, and escalation procedures.
  • Ensure data engineering team maintains audit-trail documentation for mapping changes, manual overrides, vendor setups, and store transfers with user ID and timestamp tracking.

Qualifications:
  • Bachelor's degree in information systems, Data Management, Computer Science, or related field.
  • 3-5 years of hands-on experience in data quality, data governance, or data management roles.
  • Experience with a master data management (MDM) platform; Profisee experience is strongly preferred.
  • Proficiency with Microsoft Purview (or equivalent data catalog / lineage tool) for metadata management, classification, and governance.
  • Strong SQL skills for data profiling, quality rule authoring, and ad-hoc investigation.
  • Building data quality dashboards and scorecards in Power BI.
  • Working knowledge of Microsoft Fabric for data engineering and analytics workloads.
  • Familiarity with medallion architecture (bronze / silver / gold) data design patterns, including environments where certain feeds bypass bronze/silver and arrive directly at the gold layer.
  • Familiarity with Microsoft Azure data services (Azure Data Factory, Azure SQL, Azure Data Lake / Lakehouse).
  • Excellent written and verbal communication skills; ability to translate technical quality findings into business-friendly language.

Arctiq is an equal opportunity employer. If you need any accommodations or adjustments throughout the interview process and beyond, please let us know. We celebrate our inclusive work environment and welcome members of all backgrounds and perspectives to apply.
We thank you for your interest in joining the Arctiq team! While we welcome all applicants, only those who are selected for an interview will be contacted.

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