Director of Data Analytics

210 Route 4 East Paramus, 210 Route 4 East, Paramus, NJ 07652, USA

Vornado Realty Trust (NYSE: VNO) is an equity Real Estate Investment Trust (REIT) with over 30 million square feet of office and retail properties under management. With portfolio concentration in New York City, Vornado also owns premier assets of theMART in Chicago and the 555 California Street complex in San Francisco. Vornado’s reputation in the industry is one of unmatched quality and integrity.

For more than ten years, Vornado has been a leader in environmental sustainability among REITs and large commercial landlords in New York.

As Director of Data Analytics, you will be the first dedicated analytics engineering hire and are expected to help shape and grow the data function over time, responsible for building the foundations that power data‑driven decision‑making across the company. You will partner with teams across the business to design our data models, define core metrics, and make high‑quality data easy to discover and trust.

You will formalize and scale a modern data stack, with Snowflake, dbt, and Sigma either in place or actively being implemented. Prior experience with every tool is not required; we are looking for someone who understands the underlying patterns and concepts of reliable data modeling, orchestration, testing, documentation, and self‑service analytics.

In the first 12 months, you will help establish the core warehouse architecture, create trusted financial and property‑level data marts, define key metrics in close partnership with Finance and Accounting, and support our ongoing ERP and Argus migrations by designing the data foundations and reporting needed before, during, and after implementation. The ideal candidate combines strong SQL, Python, and data modeling skills with hands‑on experience translating business questions into reliable, well‑documented data products.

This is a highly collaborative, in‑office role, with regular partnership across Finance, Accounting, IT, Leasing, Operations, and executive stakeholders.

• Design and implement scalable data models and ELT pipelines that serve as the single source of truth for financial, operational, and property‑level reporting
• Gain deep expertise in the company’s data sources during key system migrations, including the transition from the current ERP to Yardi and from on‑premise Argus Enterprise to Argus Cloud, and design models that abstract away source complexity for business users
• Integrate diverse data sources, including replicated application data, semi‑structured cloud storage data, and file‑or API‑based sources, into the warehouse, working with IT and security to establish appropriate access, compliance, and ingestion guardrails for a public company
• Establish and maintain data governance practices across the warehouse, including row‑level security in Snowflake, data classification, documentation, and auditability, coordinating with IT and security to establish access control policies
• Lead the maintenance, configuration, and ongoing improvement of data tooling across ingestion, transformation, orchestration, testing, documentation, and BI/semantic layers
• Work end‑to‑end on stakeholder requests: requirements gathering, solution design, implementation in the warehouse, documentation, and rollout to end users
• Support and enable business users by onboarding them to new data products, resolving data issues, and incorporating feedback into iterative improvements
• Define and maintain core metrics and KPI definitions (e.g., NOI, FFO, occupancy, leasing metrics, and operational KPIs) in partnership with business stakeholders

• 7+ years of experience in analytics engineering, data engineering, or closely related roles, including ownership of production data models and pipelines
• Advanced SQL, Python, and command line skills and experience for data transformation, automation, integration work, and operationalizing managed data platform components, including scripted ingestion from file‑based and API sources
• Hands‑on experience with a cloud data warehouse/lakehouse (e.g., Snowflake, Databricks) and a…