Sr Data Analyst

Mployee.me; Hyderabad; Sur place; INR 900 000 - 1 500 000; Plein temps

Résumé du poste

Mployee.me in Hyderabad, India is seeking a data analyst to review and standardize CRE data across Bronze/Silver/Gold layers, build validation checks and translate findings into actionable insights for brokers and leadership.

You will use SQL, Snowflake, Power BI or Tableau, and Python to automate QA, detect anomalies, track data lineage and deliver dashboards for stakeholders, collaborating with data engineers to fix issues at the source.

Qualifications

• 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.

Responsabilités

• Review property, availability, tenancy and comp data as it moves through Bronze, Silver and Gold layers and flag quality issues before downstream users.
• Build data validation checks, including AI-assisted checks such as anomaly detection.
• Standardize CRE data fields, such as property type, rent type, rate type and lease structure.
• 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.

Connaissances

SQL

Data analysis

Communication

Business acumen

AI/ML concepts

Outils

Snowflake

Power BI

Tableau

Python

Description du poste

• ‒ 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.