Work ArrangementThis role is based remotely, but if you live within a 50-mile radius of Atlanta, Austin, Detroit, Warren, Pontiac, Milford or Mountain View, you are expected to report to that location three times a week, at minimum.The Role The People Analytics CoE is seeking a People Analytics Solution Leader to lead our technical solutions team responsible for HR data engineering, data architecture, data automations, data science, visualizations proof of concepts, and data analytics solutions development, enhancements and management. This role requires balancing strategic infrastructure development with immediate business needs, managing both long-term architectural vision and short-term analytical deliverables. In this role, you will be responsible for setting the strategy for our HR data foundation and leading the suite of People Analytics solutions and data analytics technology, including platform management (Databricks), comprehensive data modeling, and visualization standards. These solutions will be used to inform strategic talent decisions, improve organization effectiveness, drive positive employee experience, and accelerate access to critical business insights. The role also serves as a senior technical advisor for complex, sensitive data analysis requiring deep understanding across HR, legal, and business domains. Your strong analytics technical expertise and demonstrated knowledge of different areas of HR (data, analytics, and processes) will help advance our analytics capabilities and enable data-driven decisions. What's in it for you? You will have a chance to influence our talent strategy, design the PA data infrastructure stack, help develop insights that matter, and be part of a great team that puts innovation and curiosity at the center of everything we do. Responsibilities Strategic Leadership, Data Engineering Data Architecture:Develop and implement People Analytics Solutions strategy roadmap that enhances our data engineering, self-service, AI/ML, data automation and data visualizations capabilities Build the best-in-class data environment and technology stack for the People Analytics teams (requirements, prioritization, implementation, technology) Manage and optimize platform architecture including Databricks environment configuration, resource management, and performance optimization Develop and maintain comprehensive data modeling standards across all layers (bronze/silver/gold in Databricks context) Balance delivery of immediate business insights with development of scalable long-term infrastructure Solution Implementation Governance:Lead the implementation of new analytics solutions (new analytics platforms, POC dashboards, AI models etc.) including UX training and enhancement development Establish and maintain enterprise-wide visualization standards across Power BI, Tableau, and analytics platform for consistency in HR metrics reporting Develop reusable visualization templates for key HR metrics (headcount trends, attrition analysis, talent acquisition funnels, retention etc.) Create governance frameworks for self-service reporting that balance ease-of-use with data quality and built-in quality control (in partnership with key stakeholders)Design and implement enhanced dashboard and data sharing processes, policies and standards; and security model governance framework in partnership with key stakeholder. This includes creation of role-based access controls and data visibility rules for HR dashboards Define prioritization criteria and intake process for projects and requests managed by the teamStrategic Partnerships Executive Technical Advisory:Own strategic partnerships and roadmap alignment with key HR stakeholders, IT, vendors and other technical partners to deliver best-in-class analytics tools Serve as technical lead for complex, sensitive data analysis requiring cross-domain expertise in HR, legal, and business process Develop and validate complex analytical methodologies for critical business questions requiring senior-level judgment Design and implement secure processes for handling sensitive information in our data tech stack while maintaining appropriate access controls and confidentiality Advance the AI/ML capabilities by leveraging existing use cases and using advanced modeling techniques Incorporate external data sources into internal datasets for comprehensive analysis
