Experience : 8 to 12 Years.

Required Qualifications :

- 8 to 12 years of experience as a Data Modeler or in a similar data architecture role.

- Strong domain expertise in Private Equity, Investment Management, or Asset Management (fund structures, portfolio data, investor reporting, valuations).

- Proven experience creating canonical models and developing logical & physical data models.

- Hands-on experience with Azure Databricks and Medallion architecture (Bronze/Silver/Gold layers).

- Proficiency in SQL, relational databases, dimensional modeling, and data integration patterns.

- Strong understanding of ETL/ELT pipelines and collaboration with data engineering teams.

- Excellent communication and stakeholder engagement skills to work across business and technical teams.

Must Have.

- Experience with financial data standards and regulatory reporting frameworks.

- Private Equity domain knowledge and experience.

- Familiarity with data governance, metadata, data cataloging.

- Background in cloud-based data architecture and modern data platforms.

Additional Information.

- This is 5 days work from office role (No Hybrid/ Remote option available).

- Domain expertise in Private Equity, Investment Management, or Asset Management (fund structures, portfolio data, investor reporting, valuations).

Key Responsibilities.

- Design and build canonical data models to support private equity and investment management business processes.

- Develop logical and physical data models that align with enterprise architecture and cloud strategy.

- Work within Azure Databricks Medallion architecture (Bronze/Silver/Gold layers) to design data flows and ensure consistency.

- Partner with data engineering teams to implement models and optimize for scalability and performance.

- Collaborate with business stakeholders, product owners, and analysts to gather requirements and translate them into data models.

- Ensure data standardization, lineage, and governance across applications and integrations.

- Document data models, business definitions, and metadata to support ongoing governance.

- Provide thought leadership on best practices for canonical modeling, financial data integration, and cloud data architecture.