Job Summary: 

We are seeking a Senior Data Analyst to support the Technology Obsolescence Task Force by providing actionable insights into technology lifecycle management across the enterprise. The role focuses on collecting, integrating, analyzing, and visualizing data from multiple IT sources to identify End-of-Life (EOL), End-of-Support (EOS), and obsolete technologies. The successful candidate will leverage Power BI, Tableau, SQL, CMDB, and IT Asset Management (ITAM) data to support modernization initiatives, risk mitigation, governance, and strategic decision-making. 

Key Responsibilities: 

• Collect, consolidate, and validate data from CMDB platforms (e.g., Remedy), IT Asset Management (ITAM) systems, vendor lifecycle repositories, and security, vulnerability, and patch management sources. 

• Ensure data consistency, accuracy, completeness, and alignment across asset, configuration, and lifecycle repositories. 

• Identify End-of-Life (EOL), End-of-Support (EOS), and obsolete technologies across hardware, software, operating systems, databases, middleware, and cloud platforms. 

• Perform lifecycle risk analysis based on technology type, vendor support status, business criticality, and operational impact. 

• Support remediation prioritization by assessing business risk, exposure, and technology criticality. 

• Design, develop, and maintain interactive dashboards and reports using Power BI and Tableau. 

• Create dashboards covering obsolescence risk heatmaps, lifecycle timelines, remediation progress, trend analysis, and compliance metrics. 

• Enable self-service analytics capabilities for business and technical stakeholders. 

• Support CMDB and ITAM data quality initiatives by identifying data gaps, inconsistencies, duplicate records, and ownership issues. 

• Collaborate with ITSM, ITAM, infrastructure, and platform teams to improve asset governance and lifecycle management. 

• Provide analytical insights to domain leads, project managers, delivery teams, security, risk, and audit stakeholders. 

• Translate complex technical data into clear, meaningful business insights and executive-level reporting. 

• Define reporting standards, data definitions, and governance metrics. 

• Automate reporting processes and data refresh pipelines where feasible. 

• Identify opportunities to enhance lifecycle visibility through predictive analytics and continuous process improvements.