Role : Portfolio Analytics Associate

Department : Data & Analytics (DnA)

Location : Mumbai

Department Overview :

The Data and Analytics (DnA) team consists of Investment Operations and Portfolio Analytics teams. DnA plays a critical role in supporting key functions across the organization, including Investments, Investor Relations (IR), Core Portfolio Management (Core PM), and Fund Finance. At its core, the DnA team plays foundational role in company's data ecosystem, ensuring accurate ingestion and integrity of core data - including client, managed fund, deal, investment, and asset data. This disciplined approach to data stewardship powers actionable portfolio analytics that are essential to effective investment decision-making processes and improved transparency and engagement with clients.

Portfolio Analytics - Team Overview :

This Associate will join DnA's Portfolio Analytics team to support the wider organisation in injecting analytics into all aspects of the investment lifecycle. Portfolio Analytics group is responsible for providing insightful portfolio exposure, performance, and risk data covering our entire family of funds, strategies, and products across Private Equity, Real Assets and Private Credit.

Portfolio Analytics works with technologists and business users across the firm to build both tactical analyses and strategic solutions to better equip our investors with analytics and deliver the power of company's data platform. This role will require you to work cross-functionally across our Portfolio Analytics, Data Integrity, Core Portfolio Management, Change and Technology teams.

Key Responsibilities :

Data Modelling & Analytical Problem Solving :

- Translate complex, multi-source financial datasets into scalable, well-structured analytical models.

- Analyse large datasets across funds, deals, and portfolio companies to identify trends, risks, and performance drivers.

- Build reusable datasets and data models that power downstream analytics and reporting.

Business Partnership and Insight Generation :

- Partner with Investment, Risk, and Portfolio Management teams to deliver actionable insights.

- Convert analytical outputs into clear, structured narratives for senior stakeholders.

- Own and deliver recurring analytics (e.g., performance, exposures, attribution) across asset classes.

- Support ad-hoc analytical requests from internal teams, including investor and product-related queries.

Analytics Engineering and Innovation :

- Design and build scalable data pipelines and analytical layers on Databricks.

- Develop dashboards using SQL, Python, and BI platforms (Power BI/Tableau/Looker).

- Take end-to-end ownership of analytical projects - from problem definition to delivery.

- Experiment with new tools (including Generative AI) to enhance analytical capabilities and efficiency.

Data Quality & Governance :

- Ensure integrity and consistency across multiple data sources.

- Identify and resolve data quality issues and upstream risks proactively.

- Design and implement improvements to the quality and timeliness of underling data feed and analytics deliveries.

- Document data models, business logic, and assumptions, thus maintaining institutional knowledge and consistency.

- Collaborate with Data Engineering teams to strengthen governance, controls, and lineage.

Knowledge and Experience Required :

Core Skills and Experience :

- 5 - 8 years of experience in analytics, data, or quantitative roles within financial services.

- Exposure to asset management, private markets, or capital markets (preferred but not mandatory).

- Strong SQL skills with experience working on large and complex datasets.

- Proficiency in Python / PySpark or willingness to build expertise in programming.

- Experience in data modelling for analytics or reporting use cases.

- Hands-on experience with BI/visualization tools (Power BI, Tableau, Looker, etc.).

- Strong analytical thinking, problem-solving ability, and attention to detail.

- Ability to work in a fast-paced environment and manage multiple priorities.

- Knowledge of data governance, controls, and metadata management.

Preferred (but not essential) experience :

- Experience working with Databricks, Snowflake, or similar cloud data platforms.

- Understanding of data pipelines, ETL processes, or lakehouse architectures.

- Knowledge in coding best practices, testing, version control, and automating recurring analytics processes.

- Experience with building light tools and applications using SQL, Python (e.g. Streamlit).

- Exposure to private markets datasets or fund analytics.

- Prior experience mentoring or leading small teams.