Sr. Real Estate Financial Data Analyst
The Role:
The Senior Real Estate Financial Data Analyst will lead business intelligence, analytics, reporting, and decision-support for a growing self-storage and real estate organization. Reporting to the Chief Financial Officer, this hands-on role transforms financial, operational, customer, and market data into actionable insights that improve revenue, occupancy, budgeting, forecasting, customer service, and portfolio performance. The successful candidate will be:
• Highly organized, detail-oriented, and able to connect analysis to the broader business strategy.
• Proactive, resourceful, and comfortable working independently in a fast-paced environment.
• Customer-service focused and responsive to the needs of executives, corporate teams, and field operations.
• A practical problem solver who communicates complex findings clearly to non-technical stakeholders.
• Collaborative, adaptable, accountable, and willing to work on-site in York, Pennsylvania.
Essential Duties:
Specific duties and functions of the position include, but are not limited to:
• Business Intelligence and Power BI:
o Serve as the company’s Power BI subject-matter expert; design, develop, deploy, and maintain executive, financial, operational, revenue-management, marketing, and property-level dashboards.
o Build scalable data models using Power Query, DAX, semantic modeling, automated refreshes, permissions, documentation, and quality controls.
o Replace manual spreadsheet reporting with accurate, repeatable, user-friendly reporting solutions and train employees to interpret and use the information.
• Self-Storage, Revenue Management, and Customer Analytics:
o Analyze occupancy, economic occupancy, move-ins, move-outs, achieved and street rates, discounts, concessions, delinquency, retention, lead conversion, and unit-type performance.
o Partner with operations and revenue management to evaluate pricing, rate increases, promotions, demand patterns, customer behavior, competitive conditions, and underperforming properties.
o Measure the impact of operational, marketing, pricing, and customer-service initiatives and identify opportunities to improve revenue, occupancy, retention, and net operating income.
• Budgeting, Forecasting, and Financial Analysis:
o Support annual property and portfolio budgets, monthly forecasting, variance analysis, scenario planning, and performance comparisons to budget, prior year, forecast, and underwriting.
o Analyze revenue, operating expenses, NOI, capital expenditures, and other financial measures; clearly explain significant variances and business implications.
o Assist with acquisition underwriting, due diligence, post-closing performance tracking, and integration reporting as requested.
• Data Science, Automation, and Systems Integration:
o Develop statistical, predictive, machine-learning, or AI-supported models when appropriate for revenue forecasting, pricing optimization, customer retention, lead conversion, and property performance.
o Develop, maintain, or support APIs and other integrations among operational, accounting, accounts-payable, revenue-management, and reporting platforms.
o Create reliable data pipelines and automated workflows that consolidate information, reduce duplicate entry, and eliminate unnecessary manual processes.
o Evaluate emerging analytical and AI technologies based on business value, scalability, cost, security, and user adoption.
• Data Governance, Quality, and Collaboration:
o Establish consistent KPI definitions, reconcile outputs to source systems and accounting records, troubleshoot discrepancies, and maintain documentation of data sources and methodologies.
o Protect confidential company, customer, employee, and financial information and support sound data-governance practices.
o Translate business questions into practical analytical solutions, manage competing priorities, and provide responsive service to internal customers and third-party technology partners.