• Extract performance data from source systems including Yardi, Salesforce, and internal databases. using SQL queries and data export tools.
• Use Databricks to ingest structured and unstructured data for investment analysis and reporting.
• Transform and normalize data from multiple sources to create unified datasets for performance reporting and analysis.
• Develop and maintain automated data pipelines and ETL processes to improve efficiency and reduce manual data handling.
• Prepare and format data files for NCREIF (NFI) and MSCI benchmark submissions under the direction of the Data Steward.
• Support AUM/EUM data collection processes, ensuring completeness and accuracy of assets under management reporting.
• Document data sources, transformation logic, and business rules for all recurring data processes.

• Calculate and validate investment performance metrics including IRR, MOIC, DPI, RVPI, TWR, and GAV/NAV at property, investment, and fund levels.
• Support Track Record construction by preparing data inputs and performing data transformations related to financial performance metrics.
• Assist with multi-level return aggregation calculations, ensuring accurate roll-up from property through investor levels.
• Perform variance analysis to identify discrepancies between calculated performance and source system data.
• Build and maintain Excel-based models and templates for recurring performance calculations and ad-hoc analysis.
• Generate performance dashboards and visualizations using Domo, Power BI, Tableau, or similar tools to support stakeholder reporting needs

• Execute data validation checks and reconciliations to ensure accuracy of performance data across systems and reports.
• Identify, document, and escalate data quality issues to the Data Steward with recommended remediation actions.
• Reconcile calculated performance metrics to Yardi Investment Accounting (IA) module outputs.
• Support quarterly and annual close processes by validating performance data used in investor reporting and financial statements.
• Work with the Data Steward to implement Data Governance through Databricks Unity Catalog. Maintain data quality scorecards and exception logs to track resolution of identified issues.

• Prepare data inputs and supporting schedules for Partner Reports, quarterly investor letters, and annual reports.
• Assist with Due Diligence Questionnaire (DDQ) responses by compiling performance data, track record exhibits, and supporting documentation.
• Support ad-hoc data requests, ensuring timely and accurate responses.
• Compile capital account data and investor allocation details for reporting packages in compliance with ILPA standards.
• Maintain organized files and audit trails for all investor deliverables to support compliance and regulatory requirements.
• Assist with preparation of disclosures and footnotes for investor communications.

• Support data modernization initiatives by assisting with current-state documentation, target-state data mapping, and UAT testing.
• Assist with interpretation and migration to target-state data platforms.
• Identify opportunities to automate manual processes and improve data workflow efficiency.
• Collaborate with Data Engineering and Platform teams on requirements gathering and testing for new data pipelines.
• Maintain and update process documentation, standard operating procedures, and training materials.
• Participate in system implementation projects, including requirements definition, testing, and user acceptance.

• Partner with business teams to collect and validate operational metrics and perform data reconciliations to resolve discrepancies.
• Support teams with data requests for financial reporting, valuation support, and deal analytics.
• Participate in cross-functional working groups and project teams.
• Build effective working relationships with stakeholders across Investments, Finance, and Operations to understand data needs.

• Bachelor's degree in Finance, Accounting, Economics, Data Analytics, Information Systems, or related field.
• 2–4 years of experience in data analysis, financial reporting, or investment operations; internship experience considered for entry-level candidates.
• Exposure to private equity, real estate, or alternative investments preferred; traditional asset management or fund administration experience also valued.
• Understanding of accounting principles and financial statement concepts.
• Progress toward or interest in CFA, CAIA, or CPA designation.

• Advanced Excel proficiency required: pivot tables, VLOOKUP/XLOOKUP, INDEX-MATCH, Power Query, and complex formulas.
• SQL experience required: ability to write queries to extract, filter, join, and aggregate data from relational databases.
• Experience with data visualization tools (Domo, Power BI, Tableau, or similar) for dashboard creation and reporting.
• Familiarity with Python or R for data manipulation and analysis preferred; willingness to learn required.
• Experience with Yardi or other real estate/investment management systems a plus.
• Proficiency in Microsoft Office suite, particularly Excel, PowerPoint, and Word.

Core Competencies

• Strong analytical and problem-solving skills with exceptional attention to detail.
• Ability to work with large datasets and identify patterns, anomalies, and data quality issues.
• Excellent organizational skills with ability to manage multiple priorities and meet deadlines.
• Strong written and verbal communication skills; ability to explain data findings to non-technical audiences.
• Intellectual curiosity and eagerness to learn investment concepts, fund structures, and performance measurement.
• Self-motivated with ability to work independently while knowing when to escalate issues.
• Collaborative mindset and willingness to support team members across functions.
• High degree of integrity and commitment to data accuracy and confidentiality.

• Understanding of investment performance metrics (IRR, MOIC, TWR) and how they are calculated.
• Familiarity with fund structures, waterfalls, and investor allocation methodologies.
• Experience with NCREIF, MSCI, or other industry benchmark data submissions.
• Knowledge of ILPA reporting standards and institutional investor DDQ processes.
• Exposure to data governance concepts, data dictionaries, and metadata management.
• Experience with modern data platforms (e.g., Databricks, Snowflake) or cloud-based analytics tools.
• Experience with using AI tools effectively for work tasks.