Cimgroup29d ago

Senior Data Analyst

Los AngelesFull Timesenior

Data ScienceData AnalystDataData & AI

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Quick Summary

Overview

ABOUT CIM GROUP: CIM is a community-focused real estate and infrastructure owner, operator, lender, and developer. Our team of experts works together to identify and create value in real assets,

Technical Tools

Data ScienceData AnalystDataData & AI

ABOUT CIM GROUP:

CIM is a community-focused real estate and infrastructure owner, operator, lender, and developer. Our team of experts works together to identify and create value in real assets, benefiting the communities in which we invest. Back in 1994, our three founders focused on projects in Southern California neighborhoods. Today, we are a diverse team of 900+ employees with projects across the Americas. Our projects have delivered jobs; created comfortable places to live, work, and relax; and provided necessary and sustainable infrastructure. Our focus on enhancing communities is unwavering, and we strive to make an even greater impact in the years to come. Join us and make an impact today!

POSITION PURPOSE:

The Sr. Data Analyst is responsible for extracting, transforming, validating, and analyzing investment performance data to support the firm's alternative investment reporting and decision-making processes. Working closely with the Data Steward, this role executes day-to-day data operations across fund structures, track record calculations, and investor deliverables. The Sr. Data Analyst serves as a key contributor to data quality initiatives, helping ensure accuracy and consistency of performance metrics across the investment hierarchy. This position offers exposure to real estate private equity operations, institutional investor reporting, and enterprise data modernization initiatives—providing an excellent foundation for career growth in investment data management.

Data Extraction & Transformation

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.

Performance Calculations & Analysis

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

Data Quality & Validation

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.

Investor Reporting Support

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.

Systems & Process Improvement

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.

Cross-Functional Collaboration

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.

Education & Experience

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.

Technical Skills

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.

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.

Preferred Qualifications

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.

Exceptional communication and interpersonal skills, with the ability to influence and persuade stakeholders at all levels. Strong analytical and problem-solving abilities, with a meticulous attention to detail. A proactive and collaborative mindset, with a proven ability to work effectively in a cross-functional team environment.