Sr Data Analyst

Zohorecruit; Hyderabad; On-site; INR 1,200,000 - 2,100,000; Full time

Job summary

Algoleap Technologies Pvt Ltd is seeking a senior data analyst to validate and improve CRE data quality across Bronze, Silver, and Gold layers. You will build data validation checks, standardize data fields, and translate findings into actionable insights for brokers and leadership.

The role requires 6+ years of experience, strong SQL, Snowflake, and BI tooling, plus familiarity with AI/ML techniques for data quality. Collaboration with data and AI engineers is essential.

Qualifications

• 6+ years of experience as a data analyst in data-heavy industry.
• Strong SQL skills and hands-on experience with Snowflake or similar data warehouse.
• Experience with a Medallion-like layered data architecture (Bronze/Silver/Gold).
• Track record of building and improving data quality and validation frameworks.
• Experience applying AI/ML to data quality tasks such as anomaly detection.
• Comfort with CRE-like data domains or quick domain learning.

Responsibilities

• Review property, availability, tenancy and comp data to flag quality issues.
• Build data validation checks and AI-assisted checks to catch bad data automatically.
• Standardize CRE data fields across source systems.
• Define and track data quality metrics and report to stakeholders.
• Investigate discrepancies between internal systems and external feeds.
• Analyze data to surface patterns like vacancy trends and rent growth.
• Translate findings into business insights for brokers and leadership.
• Collaborate with data and AI engineers to fix issues at the source.
• Document data lineage and transformation rules.
• Recommend AI approaches to automate data validation and deduplication.

Skills

SQL

Data analysis

Python

Power BI

Tableau

AI/ML for data quality

Tools

Snowflake

Power BI

Tableau

Python

Job description

Algoleap Technologies Pvt Ltd | Full time

• 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 dataautomatically rather than after the fact.
• Standardize CRE data fields,such as property type, rent type, rate type and lease structure, so the sameterm means the same thing across every source system.
• Define and track data qualitymetrics, including completeness, consistency, accuracy, timeliness andduplication rate, and report them to stakeholders on a regular cadence.
• Investigate discrepanciesbetween internal systems, external data feeds and source documents, and traceroot causes back to the pipeline stage that introduced them.
• Analyze property, availability,tenancy and comp data to surface patterns: vacancy trends, rent growth, tenantturnover, lease expirations and comparable sales activity.
• Translate findings intobusiness insights for brokers, researchers and leadership: build dashboards,write clear summaries and answer ad hoc questions.
• Partner with data engineers andAI engineers on upstream fixes, so quality issues get solved at the source, notpatched downstream.
• Document data lineage,transformation logic and quality rules, so the team can audit and reproduceevery number.
• Recommend AI and machinelearning approaches to automate data validation work: anomaly detection,deduplication, document classification and similar.

What You Bring

• 6+ years of experience as adata analyst, ideally in commercial real estate, financial services or anotherdata-heavy industry.
• Strong SQL skills and hands-onexperience 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 andimproving data quality and validation frameworks, not just running one-offchecks.
• Experience applying AI ormachine 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 tolearn the domain quickly.
• Strong business acumen: youconnect data findings to what they mean for brokers, asset managers andclients, not just what the numbers say on their own.
• Proficiency with a businessintelligence (BI) tool such as Power BI or Tableau, and Python for analysis andautomation.
• Clear, confident communication.You explain data problems and insights to non-technical stakeholders withoutlosing precision.

Nice to Have

• Exposure to CRE data platformsor sources, such as CoStar, RealNex, Yardi or MRI.
• Experience with a data catalogor data governance tool.