Director, Enterprise Data Systems

Overview This is a hybrid role - 2 days remote and 3 days in the Malvern, PA office. The Director of Enterprise Data Systems leads the strategic vision, architecture, and evolution of our enterprise data and analytics landscape. This role drives our continued transition from legacy architectures to a high-performing Databricks Lakehouse platform while evolving our business intelligence ecosystem. A foundational focus is empowering enterprise-wide data democratization through continuous self-service discovery using natural-language Genie Agents. Additionally, this leader is accountable for orchestrating our cloud-first analytics strategy through the migration of our reporting layer to a cloud based BI platform. As a core strategic partner, you will work across the organization to collaborate with business teams, modernizing and scaling how the company uses data to perform analytics and develop and execute models to make strategic decisions. Company Overview CubeSmart (NYSE: CUBE) is a publicly held, self-administered and self-managed real estate investment trust focused on the ownership, operation, acquisition, and development of self-storage facilities in the United States. The company’s mission is to simplify the organizational and logistical challenges created by the many life events and business needs of its customers- through innovative solutions, unparalleled service, and genuine care. CubeSmart is intentional about culture. You can experience it everywhere from the mission statement of “genuine care” to the “It’s what’s inside that counts” tagline to calling each other “teammates” rather than employees. This spirit fosters a fun and collaborative environment that is filled with over 2,600 teammates across 1,010 self-storage facilities in 35 states, including the District of Columbia, providing unparalleled service to our 480,000 customers. Responsibilities Visionary Leadership Define and champion the enterprise data vision : Establish a forward-looking strategy for how the Databricks platform can evolve beyond traditional data and analytics to enable AI, advanced analytics, automation, and new data-driven business capabilities. Drive innovation and platform evolution : Identify and evaluate new Databricks capabilities, architectural patterns, and emerging technologies that can simplify the data ecosystem, accelerate delivery, and create measurable business value. Translate business strategy into data-enabled opportunities : Partner with technology and business leaders to identify high-impact use cases, challenge conventional approaches, and build a roadmap that positions the enterprise data platform as a strategic enabler of growth, efficiency, and competitive advantage. AI-Driven Data Democratization & Databricks Genie Agents Genie Agent Architecture: Construct, manage, and continuously improve domain-specific Databricks Genie Agents, allowing end business users to query production enterprise metrics natively using natural language. Unified Metric Layer: Build and oversee semantic layers (Genie Ontology) ensuring both structured reporting dashboards and unstructured conversational Genie spaces reference mirrored, trusted operational definitions. Self-Service Playbook: Evangelize data democratization guidelines mapping distinct business workloads to correct channels (guided visuals in the BI Platform vs. conversational analytics in Databricks Genie). Business Analytics Collaboration & Machine Learning Model Modernization Cross-Functional Cross-Over: Serve as the primary enterprise data partner to corporate Data Science and analytics teams, providing engineering resources and optimized cloud data streams. Machine Learning Model Scale: Continued modernization of the deployment, model lineage, processing frequency, and serving architecture of algorithms used to compute product/service pricing across the corporate footprint. MLOps Lifecycle Management: Enforce strict MLOps principles within Databricks (MLflow, Feature Stores, Unity Catalog) to scale feature engineering and tracking for machine learning models. Modern Analytics Infrastructure & Cloud Migration BI Cloud Migration: Lead and finalize the comprehensive cloud migration strategy moving legacy, on-premises BI applications to the Cloud BI platform. BI Infrastructure Standardization: Standardize the technology used across the internal and external facing BI platforms for orchestration and distribution of complex reporting solutions. SaaS Administration & Security: Establish architecture, stream management, workspace access, and licensing optimization protocols specific to the adoption of a cloud first BI platform. Project Execution and Delivery Evolving business capabilities: Manage a high performing team to deliver business capabilities through the Databricks platform to enhance and evolve the capabilities of the business to use data to drive value. Continued engineering innovation: Foster a culture of continuous innovation to evolve the data platform's core capabilities. Accelerate the onboarding of next-generation data workloads, such as contact center analytics and rich media (audio/video), enabling CubeSmart to extract maximum value from unstructured data ecosystems. Financial system modernization: Work with vendors and partners to enable CubeSmart to evolve financial analytics capabilities associated with a migration to a new ERP platform. Governance, Security, & Optimization Unity Catalog Governance: Enforce data governance ensuring row/column permission models applied inside Databricks are accurately inherited by Genie Agents and BI Platform users. Financial FinOps: Track, evaluate, and optimize consumption metrics, scaling thresholds, and computational costs across Databricks nodes and cloud BI software tiers. Qualifications Professional Background: 10+ years hands-on managing data infrastructure/engineering teams with 3+ years in cross-functional technical leadership roles. Databricks Expertise: Hands-on experience deploying workflows over Databricks platform, Apache Spark frameworks, Data Lakehouse structures, and Unity Catalog. BI Architecture: Proven track record moving visualization spaces into cloud architectures, with experience in the migration of on-premises BI capabilities to a cloud-based solution. Data Science Acumen: Firm architectural knowledge of data science execution, statistical model lifecycle tracking, and technical pipelines required to run enterprise models. Stakeholder Management: Demonstrable experience successfully partnering with business stakeholders to deliver high-value business initiatives. Employee Development and Teambuilding: Proven track record of developing others and building high-performance teams that achieve. Educational Requirements: Bachelor’s or master’s degree in Computer Science, Engineering, Statistics, Information Systems, or a related field. Candidates must be authorized to work in the U.S. without the need for current or future sponsorship. We are an Equal Opportunity Employer, Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity

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