Data Governance & Quality Analyst

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