Data Modeler

Experience:

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
  

• 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