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WHAT YOU’LL DO
Data & Data Platforms
- Define and execute the Workplace Solutions data-platform strategy in alignment with business priorities and enterprise data direction.
- Lead data-engineering capabilities that support trusted operational and analytical data, including reusable data products, data pipelines, business-owned semantics, and self-service access.
- Advance AI-ready data practices, including data quality, metadata, lineage, governance, security, lifecycle management, and clear accountability for data contracts.
- Guide the evolution and adoption of data capabilities supporting Member 360, MongoDB-based solutions, analytical data, reporting, advanced analytics, and future AI use cases.
- Partner with business and technology stakeholders to prioritize data investments based on measurable customer, associate, operational, and delivery outcomes.
Artificial Intelligence
- Define the Workplace Solutions AI enablement strategy and translate business and technology opportunities into an actionable portfolio of AI capabilities.
- Lead the incubation, validation, and responsible scaling of AI solutions that improve customer experience, associate productivity, decision quality, operational efficiency, and software delivery.
- Establish reusable AI capabilities, including governed skills, agents, knowledge solutions, engineering instructions, and patterns that can be adopted across teams.
- Embed appropriate security, privacy, compliance, human oversight, measurement, and lifecycle controls into AI experimentation and production use.
- Partner with enterprise AI, architecture, cybersecurity, data, legal, risk, and business teams to align solutions with enterprise standards and Workplace Solutions needs.
Software Engineering & Developer Enablement
- Lead the Workplace Solutions software engineering practice and developer ecosystem, creating a consistent foundation for how teams design, build, test, secure, release, operate, and improve software.
- Advance engineering standards, golden paths, repository templates, reusable workflows, architecture context, developer self-service, and practical enablement that make preferred practices easier to adopt.
- Improve engineering flow through modern source-control practices, CI/CD, build-once and promote-many patterns, automated quality and security checks, progressive delivery, and measurable delivery health.
- Enable AI-assisted engineering across design, development, code review, testing, knowledge discovery, modernization, and repository transferability while applying shared engineering expectations.
- Use engineering intelligence, developer feedback, DORA and flow measures, quality signals, and adoption data to identify friction and prioritize improvements.
- Partner with application leaders and ART teams to prove new practices on real work, capture reusable patterns, and scale successful capabilities.
Technology Innovation
- Establish a disciplined innovation model for identifying emerging technologies and validating their potential value for Workplace Solutions.
- Create a clear progression from idea to proof of value, pilot, productization, adoption, and outcome measurement.
- Build small, focused experiments that address real business or engineering problems and define explicit success, risk, and scaling criteria.
- Connect successful innovations to durable ownership, support models, architecture standards, funding, and adoption plans rather than leaving them as isolated proofs of concept.
- Maintain awareness of emerging capabilities in data, AI, cloud, software engineering, automation, and developer tooling, and communicate their relevance, trade-offs, and readiness to leaders.
Platform Portfolio & Stakeholder Leadership
- Manage Data, AI, Software Engineering, and Innovation capabilities as a coherent platform portfolio with clear roadmaps, priorities, service expectations, adoption measures, and investment decisions.
- Build strong partnerships across Workplace Solutions business functions, ART leadership, product management, architecture, cybersecurity, infrastructure, operations, and enterprise platform teams.
- Clarify boundaries and decision rights between enterprise platforms, Workplace Solutions platforms, ART delivery responsibilities, and business accountability.
- Communicate strategy, progress, risks, trade-offs, and outcomes clearly to executives, technology leaders, delivery teams, and business stakeholders.
- Guide vendor, tooling, capacity, sourcing, and financial decisions with a focus on reuse, sustainability, security, measurable value, and total cost of ownership.
Team Leadership & Development
- Lead, coach, and develop multidisciplinary leaders and teams across data engineering, AI, software engineering enablement, and innovation.
- Build a culture of customer focus, technical excellence, accountability, experimentation, inclusion, learning, and continuous improvement.
- Develop technical and leadership capabilities, succession plans, workforce strategies, and communities of practice aligned to the capability roadmap.
- Create an environment where teams can move quickly in discovery and experimentation while applying the rigor required for sustainable production capabilities. Promote a culture of diversity and inclusion, value different ideas and opinions, and listen courageously, remaining curious in all that you do.
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WHAT YOU’LL BRING:
- 8+ years of progressive experience across software engineering, data engineering, artificial intelligence, platform engineering, technology innovation, architecture, or related disciplines.
- 5+ years of people leadership experience, including responsibility for technical leaders or multidisciplinary teams and organization-level outcomes.
- Demonstrated experience leading shared technology capabilities, engineering practices, platforms, centers of excellence, or enablement services used by multiple product or delivery teams.
- Strong experience in software engineering and at least two of the following areas: data engineering and analytics, AI/ML enablement, developer platforms and DevOps, cloud engineering, or technology innovation.
- Experience defining strategy, building roadmaps, sequencing pilots, productizing proven capabilities, managing investments, measuring outcomes, and driving adoption across organizational boundaries.
- Working knowledge of modern software delivery, cloud and data architectures, AI governance, security, operational risk, and technology lifecycle management.
- Excellent communication, coaching, stakeholder-management, strategic-planning, and organizational-change skills.
- Able to work remotely with access to a high-speed internet connection and located in the United States or Puerto Rico.
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PREFERRED:
- Experience in group insurance, financial services, Workplace Solutions, or another regulated enterprise environment.
- Experience operating within SAFe or another scaled product-delivery model with multiple Agile Release Trains.
- Experience with modern data platforms, MongoDB, analytical data products, semantic models, data governance, and AI-ready data practices.
- Experience with generative AI, reusable agents and skills, responsible AI, AI-assisted software engineering, knowledge systems, or AI-enabled modernization.
- Experience with GitHub Enterprise, GitHub Actions, developer portals, cloud services, automated testing, artifact management, observability, and engineering intelligence platforms.
- Bachelor’s degree in computer science, engineering, information systems, data, or a related field; advanced degree or relevant certifications are beneficial.
This role is classified as remote; however, periodic in-person attendance will be required at times for activities such as quarterly or project planning, training, or other business-critical meetings.
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