Data Governance & Quality Analyst

Arctiq•Philadelphia, PA

1d•Remote

About The Position

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.

Requirements

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.

Nice To Haves

Profisee experience is strongly preferred.

Responsibilities

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.
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.
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).
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.
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.
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.
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.