SPECTRAFORCE

Data Quality & Asset Specialist

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Quality & Asset Specialist in Raleigh, hybrid, for 12 months at a pay rate of "unknown." Key skills include data stewardship, Google Sheets, and Tableau expertise, with a focus on data integrity and lifecycle management.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
256
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πŸ—“οΈ - Date
March 3, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Raleigh, NC
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🧠 - Skills detailed
#Data Lifecycle #Compliance #Data Stewardship #Visualization #Strategy #Data Analysis #Data Accuracy #Data Cleaning #Data Integrity #Tableau #Pivot Tables #Data Quality
Role description
Job Title: Data Analyst II Location: Raleigh, Hybrid (work onsite at Data Centers office or at the client's Tower ) Duration: 12 months Job Description Data Quality & Asset Specialist Job Summary We are looking for a Data Quality & Asset Specialist to manage the integrity and reporting of our physical and logical infrastructure data within client’s Global Data Center footprint. You will be responsible for ensuring that our data center asset records are accurate, audited, and aligned with our Information Lifecycle Management (ILM) standards. While you work within the Data Center Operations team, your work provides the strategic visibility needed to manage risk and plan for future capacity. Key Responsibilities 1. Data Quality, Profiling & Auditing CDE Governance: Monitor and validate Critical Data Elements (CDEs)β€”including rack utilization, power capacity, and hardware lifecycle datesβ€”to ensure they meet defined quality thresholds. State of Assets Reporting: Develop and manage a regular cadence of State of the Union reports via Tableau and Google Sheets, providing the Sr. Manager with a clear view of inventory health. Data Scrubbing: Identify and resolve unidentified assets (equipment that is plugged in but not documented) or orphaned records in the tracking system. Standardization: Enforce naming conventions and data entry standards across global sites to ensure reporting consistency. Integrity Reporting: Develop KPIs and dashboards that reflect the health and accuracy of the inventory data. 1. Strategy Support and Planning ILM Collaboration: Support the ILM Program Manager by implementing data standards and ensuring that the data center’s physical assets comply with broader organizational data lifecycle policies. Data Stewardship: Act as a Data Steward for infrastructure assets, maintaining high data integrity regardless of the underlying toolset. Capacity Insights: Provide accurate data on available rack space, power, and cooling to assist in future growth planning. Cross-Functional Liaison: Serve as the bridge between the boots-on-the-ground DC technicians and the high-level ILM compliance requirements. 1. Risk Management & Mitigation Identify aging assets that are reaching End of Life (EOL) or End of Service Life (EOSL) to prevent hardware-failure risks. Root Cause Remediation: Conduct investigations into data drifts and work with the operations team to fix manual entry processes at the source. Required Technical and β€œSoft” Skills Lifecycle Standards: Understanding of industry-standard frameworks for Data Quality and Lifecycle Management (e.g., DAMA or similar), moving beyond simple data entry to true data stewardship. Google Sheets: Mastery of advanced formulas, pivot tables, and data cleaning techniques Tableau: Ability to build, maintain, and automate complex data visualizations. Traits High-grit attention to detail Possesses a methodical mindset with a high tolerance for repetitive, high-volume tasks. You understand that the macro-level strategy is only as good as the micro-level data, and you take pride in the 'boots-on-the-ground' accuracy required to bridge that gap Collaborative Stewardship: Strong interpersonal skills to investigate data discrepancies with technicians without assigning blame, fostering a cooperative culture of data accuracy.