Real Estate Data Quality Assurance Associate

You will be part of a team responsible for building and maintaining a trusted, high-integrity real estate fund dataset. The team gathers information from diverse public and manager-provided sources, validates it through rigorous data-quality and portfolio quality-assessment processes, and continuously automates the data pipeline to improve accuracy, timeliness and coverage. AI tools and internally built applications are used extensively across every stage of this work, and the team is expected to adopt, apply and help improve these tools as a core part of day-to-day delivery.

• →Gather real estate fund information from diverse public sources and directly from fund managers, ensuring complete and well-documented coverage.
• →Ensure the accuracy, reliability and integrity of collected data through rigorous, repeatable validation and quality-assurance processes.
• →Apply, maintain and improve data-quality frameworks, validation rules and outlier-detection methodologies to safeguard data integrity at both record and portfolio level.
• →Carry out portfolio quality assessment – reviewing portfolios for completeness, consistency, structural integrity and reasonableness, and flagging and resolving issues before sign-off and publication.
• →Apply a high level of attention to detail to identify, investigate and resolve outliers, anomalies and inconsistencies before data is published.
• →Communicate effectively with clients and fund managers – gathering detailed information using standardized questionnaires, responding to queries, clarifying methodology and building strong, professional working relationships.
• →Make extensive use of AI tools and automation to accelerate data collection, validation and quality checks; this role requires the daily, hands-on use of AI and related tools as a primary way of working.
• →Contribute to the design and streamlining of the data flow, and help build and enhance the automation of the data collection and quality-assurance process.
• →Analyse data trends and patterns to support data-quality decisions and provide clear, well-reasoned explanations to internal stakeholders and clients.
• →Document processes, controls and validation logic to support consistency, auditability and knowledge sharing across the team.

• Strong analytical capabilities, with the ability to interpret, cross-check and draw sound conclusions from complex datasets.
• Exceptional attention to detail and a genuine commitment to data quality and data integrity.
• Familiarity with data-quality frameworks, validation rules, outlier methodologies and portfolio quality assessment is highly valued.
• Excellent client-facing communication skills, both written and verbal, with the confidence to engage fund managers and clients directly.
• Comfort and proficiency with AI tools is an absolute must – the role requires extensive, daily use of AI and automation tools, and a willingness to continuously learn and adopt new ones.
• Proficiency in MS Excel and familiarity with data and automation tools.
• A good understanding of private fund mechanisms and operations.
• A structured, process-oriented mindset with strong organizational skills.
• A background in data science or a related quantitative field is advantageous.
• At least 3 years of relevant work experience.

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