‒         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.