In this article, Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021) shares his professional experience as Business & Data Analyst at Tikehau Capital.
About the company
Tikehau Capital is a global alternative asset management group headquartered in Paris, France. Founded in 2004, it has become a multi-billion-euro investment house with expertise across private debt, real assets, private equity, and capital markets strategies. The company manages assets for institutional and private investors worldwide, relying on a long-term investment philosophy and strong entrepreneurial culture.
Tikehau Capital is a public company listed on Euronext Paris and operates in over a dozen countries. Its diversified investment strategies and robust risk management have contributed to consistent growth and resilience across market cycles.
Logo of Tikehau Capital.
Source: Tikehau Capital.
I worked in the Information & Technology (IT) department of Tikehau Capital, and collaborated extensively across various teams within the organisation. My projects focused on building tools and processes that directly supported investment decision-making and portfolio monitoring.
My Experience
As a Business & Data Analyst in the IT department, my role was to design, develop, and deploy technology solutions for the company. I collaborated with several stakeholders such as Investment team, Risk, Operations, Sales and Marketing, Client Services and Top Management. Within the Investment teams I touched on topics across the TKO strategies, Private Debt, Private Equity, Tactical Strategies, Real Assets and Capital Market Strategies. This included working closely with investment professionals to understand their analytical needs and then translating those into scalable, automated systems for data processing, quantitative analysis and reporting.
My missions
As the technical lead and subject matter expert for a major digitisation project in an agile environment, I drove revenue and productivity enhancements through automation, data analysis, and improvements in data quality and reporting. My role involved analysing TKO’s portfolio of private investments, building automated reporting engines, developing quantitative analytics modules for portfolio monitoring, and creating ETL pipelines to consolidate data from multiple internal and external sources. I collaborated extensively across functions—including Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, Investment, Private Debt, Private Equity, and Real Estate—to ensure the successful delivery of technology solutions aligned with business needs.
Key projects included: developing quarterly investor reporting automation tools for several business units (Python, Databricks, Microsoft PowerPoint, Word, Excel); leading the private markets data migration to Databricks and introducing company-wide KPI harmonisation, boosting efficiency by 12x; implementing dashboards for use by both cross-functional teams and top management (Databricks, PowerBI); and automating report delivery to clients (Power Automate). I also led analysts through project lifecycles, providing coaching to both junior and senior team members on in-house and external tools.
From an analytics perspective, I delivered asset performance analyses across funds and asset classes, supported fundraising and investor onboarding analytics, conducted risk assessments on asset performance under economic stress, and developed in-house benchmarking for Private Debt funds in collaboration with external partners. On the investor relations side, I handled institutional investor performance reporting requests. For data quality, I monitored internal platforms, managed escalation processes, and mitigated risks, issues, and dependencies to maintain the integrity and reliability of critical datasets.
Required skills and knowledge
In my role at Tikehau Capital, I developed a combination of technical, analytical, and cross-functional skills that enabled me to deliver technology solutions supporting investment decision-making across multiple asset classes. One of the key technical skills I applied was advanced programming in Python and SQL, which I used to design ETL pipelines, automate reporting processes, and integrate data from multiple sources into Databricks. I also gained deep expertise in dashboard creation and visualisation using PowerBI, allowing me to present complex portfolio performance metrics in a clear and actionable format for both senior management and investment teams.
I strengthened my understanding of private markets data structures, particularly within private debt, private equity, and real estate. This included learning how to manage and standardise KPIs across the organisation to ensure consistency in reporting and analysis. My work required strong knowledge of data governance and quality control, as I was responsible for monitoring internal platforms, managing data integrity issues, and implementing processes to improve accuracy and reliability.
On the soft skills side, I honed my ability to gather business requirements from diverse stakeholders and translate them into technical specifications. This meant working closely with colleagues from Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, and Investment teams in an agile environment. I also developed leadership skills by guiding analysts through the project lifecycle and providing coaching to both junior and senior professionals on the use of in-house and external tools. Finally, I gained significant experience in investor relations support by preparing data for performance reporting, responding to institutional investor requests, and ensuring clear, professional communication of complex investment information.
What I learned
One of the most valuable lessons I learned at Tikehau Capital was how technology teams can act as strategic partners to investment teams across multiple asset classes, including Private Debt, Private Equity, Capital Market Strategies (CMS), Tactical Strategies, and Real Assets. Working in the IT department while collaborating closely with investment specialists taught me how to align technical solutions with diverse investment strategies and operational requirements. For example, during the digitisation project, I learned how to translate complex business requirements from each asset class into scalable automation, analytics, and reporting tools that directly improved portfolio monitoring, decision-making, and investor communication.
I gained a deeper understanding of asset class–specific analytics: in Private Debt and Private Equity, I worked on performance tracking, KPI harmonisation, and risk analytics; in CMS and Tactical Strategies, I learned how derivative positions and macro-driven strategies required different data models and stress-testing frameworks; and in Real Assets, I helped design systems that tracked physical asset performance alongside market and operational metrics. This cross-asset exposure enhanced my ability to adapt technical workflows to varied investment approaches.
From a technical perspective, I refined my proficiency in Python, SQL, Databricks, and PowerBI, using them to build ETL pipelines, automation workflows, and dashboards that served both analysts and senior management. I also honed my problem-solving skills by identifying bottlenecks in reporting processes and implementing solutions that improved efficiency by more than 10x in some areas. Additionally, I learned the importance of outcome evaluation—ensuring that every dataset, whether for an internal management dashboard or an institutional investor report, was accurate, consistent, and presented in a clear, actionable format tailored to the needs of each asset class.
Financial concepts related to my internship
I present below three financial concepts related to my internship and how they were applied in my work at Tikehau Capital: Portfolio monitoring, Credit risk metrics, and Cash flow forecasting.
Portfolio monitoring
Portfolio monitoring is the ongoing process of tracking an investment portfolio’s performance, risk profile, and compliance status to ensure it aligns with its strategic objectives. This involves assessing metrics such as returns, volatility, drawdowns, asset allocation shifts, and adherence to investment guidelines. Effective portfolio monitoring enables timely decision-making, allowing managers to rebalance, hedge, or adjust positions in response to market movements or changes in portfolio objectives. During my time in the IT department working closely with investment specialists across private debt, private equity, CMS strategies, tactical asset allocation, and real assets, I learned how technology can streamline this process. The reporting engines I built automated large parts of the workflow—integrating data from multiple sources, applying valuation models, and generating performance dashboards—allowing investment teams to access accurate, real-time insights without the delays and potential errors of manual data handling. This experience deepened my understanding of how portfolio monitoring supports not only performance measurement but also risk management, regulatory compliance, and informed strategic decision-making.
Credit risk metrics
Credit risk metrics are quantitative and qualitative measures used to evaluate the likelihood that a borrower will default on their obligations and to estimate the potential loss to the portfolio in such an event. These metrics include probability of default (PD), loss given default (LGD), exposure at default (EAD), credit spreads, and internal credit ratings, all of which help investment teams assess both individual counterparty risk and overall portfolio vulnerability. Accurate credit risk assessment is essential for pricing loans, structuring debt instruments, and determining capital allocation. While working closely with investment specialists across private debt, private equity, CMS strategies, tactical strategies, and real assets, I developed tools that integrated multiple credit risk data feeds into interactive dashboards. These systems consolidated financial statement data, market indicators, and external credit ratings into a unified view, enabling faster and more reliable risk assessments. By automating data aggregation and providing visual, real-time insights, these tools not only improved assessment accuracy but also allowed portfolio managers to respond more proactively to emerging credit concerns.
Cash flow forecasting
Cash flow forecasting is the process of estimating the timing and magnitude of future inflows and outflows for an investment or portfolio. It is essential for assessing expected returns, ensuring sufficient liquidity to meet obligations, and supporting capital allocation decisions. Accurate forecasting allows investment teams to anticipate funding needs, optimise debt schedules, and stress test portfolios under different market conditions. This is particularly important in asset classes such as private debt, private equity, CMS strategies, tactical strategies, and real assets, where cash flows can be irregular and heavily dependent on deal structures, economic cycles, and market events. To support this, I built ETL pipelines that extracted deal-level data from multiple internal and external sources, transformed it into a consistent structure, and integrated it with dynamic forecasting models. These pipelines enabled investment teams to perform real-time scenario analyses, adjusting for variables such as interest rate changes, market shocks, and asset performance trends. By automating data preparation and linking it directly to forecasting tools, the process became faster, more transparent, and more adaptable to shifting market conditions.
Why should I be interested in this post?
For ESSEC students interested in finance and technology, this experience shows how a role in IT within an investment firm can offer direct exposure to financial markets, portfolio analytics, and data-driven decision-making—skills highly valuable in both finance and quantitative career paths.
Related posts on the SimTrade blog
▶ All posts about Professional experiences
▶ Alexandre VERLET Classic brain teasers from real-life interviews
Useful resources
Academic articles
Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years, Journal of Banking & Finance, 21(11–12):1721–1742.
DeFond, Mark L. & Hung, Mingyi. (2003). An empirical analysis of analysts’ cash flow forecasts, Journal of Accounting and Economics, 35(1):73–100.
Business resources
Tikehau Capital Solutions
Preqin Preqin – Alternative data platform
BlackRock eFront – Portfolio Management Solution
About the author
The article was written in August 2025 by Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021).