GP Associate
S&P Global
- Location
- Kuala Lumpur MYS
- Work model
- On-Site
- Level
- Entry
- H-1B history
- 10 approvals (FY2023)
- Posted
- 4h ago
About this role
About the Role
Grade Level (for internal use): 07 Overview: We are seeking a detail-oriented, analytical, and proactive GP Associate to join our Private Capital GP Managed Data Services team within a dynamic global SaaS environment. This role sits at the intersection of private markets, financial data, and data operations, supporting Venture Capital and Private Equity clients through the ingestion, standardization, and analysis of critical financial and capitalization table data. You will help ensure high data accuracy on our iLEVEL platform, contribute to successful implementations, and support reliable service delivery for institutional clients. This role is ideal for someone who enjoys working with financial data, has strong attention to detail, and wants to build experience in private markets, data management, and SaaS-based client delivery within a fast-paced MNC environment.
What you will work on
Financial Data Standardization & Ingestion: Process, standardize, and upload complex financial statements, including Income Statements, Balance Sheets, and Cash Flow Statements, for Venture Capital portfolio companies into our proprietary data platforms using standardized mapping frameworks. Financial & Legal Document Analysis: Analyze key corporate and legal documents—including Articles of Incorporation, Capitalization Tables, and Share Purchase Agreements—to accurately extract and verify economic rights, share classes, and ownership structures. Platform Implementation & Workflow Execution: Support the onboarding and implementation of our Private Capital Markets platform for client firms. Work across multiple client data streams and reporting cycles to ensure timely, accurate, and scalable data delivery. Process Documentation & Quality Control: Apply rigorous internal controls to maintain strict data integrity. Assist in documenting operational workflows and data transformation rules to support scalable data management processes. Project Support & Operational Collaboration: Collaborate closely with Service Delivery Managers and cross-functional teams on client projects, providing operational execution and technical data support to deliver reliable outcomes in a fast-paced client service environment. What we look for :
Education & Experience
Bachelor’s degree in Finance, Accounting, or a related field. A professional accounting qualification such as CPA or ACCA would be beneficial. Open to high-potential Fresh Graduates or candidates with 1–2 years of experience in data management, financial analysis, or managed services. Financial & Domain Knowledge: Solid foundational understanding of financial statements, corporate reporting, and fundamental accounting principles. Familiarity with Venture Capital, Private Equity, or private capital market structures is a strong plus. Precision & Attention to Detail: Strong analytical skills with a clear focus on data accuracy. Ability to review complex legal and financial documents carefully, identify data gaps or inconsistencies, and document processes clearly. Mindset & Work Ethic: A highly accountable, hands-on work ethic with a strong desire to take ownership of tasks. Comfortable working independently in a fast-paced environment, with the flexibility to manage increased workload during peak operational periods such as quarterly reporting cycles. Problem Solving & Ownership: Able to manage multiple priorities, investigate root causes, propose practical solutions, and take ownership of assigned work in a fast-paced environment. Communication & Collaboration: Excellent verbal and written communication skills. Strong team player who thrives under tight deadlines, maintains a positive attitude, and demonstrates solid business acumen. Technical Skills & Data Mindset: Comfortable learning new technologies and navigating different tools and platforms. Curious about data, able to work with structured information, and interested in improving how data