Sr Data Analyst

‒ Review property, availability, tenancy and comp data as it moves through the Bronze, Silver and Gold layers, and flag quality issues before they reach downstream users.

‒ Build data validation checks, including AI-assisted checks such as anomaly detection, that catch bad data automatically rather than after the fact.

‒ Standardize CRE data fields, such as property type, rent type, rate type and lease structure, so the same term means the same thing across every source system.

‒ Define and track data quality metrics, including completeness, consistency, accuracy, timeliness and duplication rate, and report them to stakeholders on a regular cadence.

‒ Investigate discrepancies between internal systems, external data feeds and source documents, and trace root causes back to the pipeline stage that introduced them.

‒ Analyze property, availability, tenancy and comp data to surface patterns: vacancy trends, rent growth, tenant turnover, lease expirations and comparable sales activity.

‒ Translate findings into business insights for brokers, researchers and leadership: build dashboards, write clear summaries and answer ad hoc questions.

‒ Partner with data engineers and AI engineers on upstream fixes, so quality issues get solved at the source, not patched downstream.

‒ Document data lineage, transformation logic and quality rules, so the team can audit and reproduce every number.

‒ Recommend AI and machine learning approaches to automate data validation work: anomaly detection, deduplication, document classification and similar.

What You Bring ‒ 6+ years of experience as a data analyst, ideally in commercial real estate, financial services or another data-heavy industry.

‒ Strong SQL skills and hands-on experience with Snowflake or a comparable cloud data warehouse.

‒ Experience with a Medallion (Bronze, Silver, Gold) or similar layered data architecture.

‒ A track record of building and improving data quality and validation frameworks, not just running one-off checks.

‒ Experience applying AI or machine learning to data quality work: anomaly detection, entity resolution, document extraction or similar.

‒ Comfort working with property, availability, tenancy and comparable sales (comps) data, or the ability to learn the domain quickly.

‒ Strong business acumen: you connect data findings to what they mean for brokers, asset managers and clients, not just what the numbers say on their own.

‒ Proficiency with a business intelligence (BI) tool such as Power BI or Tableau, and Python for analysis and automation.

‒ Clear, confident communication. You explain data problems and insights to non-technical stakeholders without losing precision.

Nice to Have ‒ Exposure to CRE data platforms or sources, such as CoStar, RealNex, Yardi or MRI.

‒ Experience with a data catalog or data governance tool.