Strategic architecture and vision: Define and drive the REMS data platform strategy, consolidating fragmented data sources and legacy pipelines into a unified, governed, and scalable architecture that serves as the foundation for analytics, AI, and operational decision-making across real estate management.
Technical leadership: Provide hands-on technical leadership across the REMS Data Engineering team and related initiatives — setting architectural standards, design patterns, and engineering best practices that raise the quality bar across the organization and ensure alignment with enterprise platform standards.
Data platform delivery: Oversee the design, delivery, and continuous improvement of REMS data pipelines, APIs, and backend data services that ingest, transform, and serve property, lease, facilities, and operational data to downstream products, analytics, and AI systems.
Data modeling and architecture: Own data modeling standards across REMS — including relational, dimensional, and NoSQL schemas — ensuring data structures are designed for performance, maintainability, and reliable consumption by business intelligence, data science, and application teams.
Data governance and quality: Establish DataOps practices, data quality frameworks, lineage tracking, and compliance controls that ensure REMS data products are production-ready, auditable, and trusted — with clear ownership models and monitoring across all data assets.
Enterprise integration and API strategy: Architect integration patterns and API strategies that enable seamless data access across REMS applications, analytics platforms, and enterprise systems — including event-driven patterns and consumption standards for both internal and external data consumers.
Cross-functional leadership: Partner with REMS Product, Application Engineering, Enterprise Data, and business stakeholders to align data platform capabilities with product strategy and operational priorities — translating complex data challenges into actionable roadmaps with measurable outcomes.
Team development and mentorship: Hire, develop, and retain data engineers and team leads across all levels; conduct architecture and delivery reviews, provide technical guidance, and build a team culture defined by ownership, curiosity, and continuous improvement.
Stakeholder management: Serve as the data engineering voice in product reviews, architecture forums, and executive presentations — communicating roadmap, trade-offs, and technical direction with clarity and confidence to both technical and business audiences.