Role & Responsibilities
drives large-scale data modernization and AI readiness for global enterprises. We are looking for an experienced Data Modeler to design, standardize, and maintain enterprise data models across our modernization initiatives — ensuring consistency, quality, and business alignment across cloud data platforms.
The person will be responsible for translating business requirements and data flows into robust conceptual, logical, and physical data models across multiple domains (Customer, Product, Finance, Supply Chain, etc.). You will work closely with Data Architects, Engineers, and Governance teams to ensure data is structured, traceable, and optimized for analytics and interoperability across platforms like Snowflake, Dremio, and Databricks.
Key Responsibilities-
- Develop conceptual, logical, and physical data models aligned with enterprise architecture standards.
- Engage with Business Stakeholders: Collaborate with business teams, business analysts and SMEs to understand business processes, data lifecycles, and key metrics that drive value and outcomes.
- Value Chain Understanding: Analyze end-to-end customer and product value chains to identify critical data entities, relationships, and dependencies that should be represented in the data model.
- Conceptual and Logical Modeling: Translate business concepts and data requirements into conceptual and logical data models that capture enterprise semantics and support analytical and operational needs.
- Physical Data Modeling: Design and implement physical data models optimized for performance and scalability
- Semantic Layer Design: Create semantic models that enable business access to data via BI tools and data discovery platforms.
- Data Standards and Governance: Ensure models comply with enterprise data standards, naming conventions, lineage tracking, and governance practices.
- Implement naming conventions, data standards, and metadata definitions across all models.
- Collaboration with Data Engineering: Work closely with data engineers to align data pipelines with the logical and physical models, ensuring consistency and accuracy from ingestion to consumption.
- Manage version control, lineage tracking, and change documentation for models.
- Participate in data quality and governance initiatives to ensure trusted and consistent data definitions across domains.
- Create and maintain a business glossary in collaboration with the governance team.
Ideal Candidate
- Strong Enterprise Data Modeller profile (Modern Data Platforms)
- Mandatory (Experience 1) – Must have 7+ years of experience in Data Modeling or Enterprise Data Architecture, with strong hands-on expertise in designing conceptual, logical, and physical data models for enterprise data platforms
- Mandatory (Experience 2) – Must have Strong hands-on experience with enterprise data modeling tools such as Erwin, ER/Studio, PowerDesigner, SQLDBM, or similar enterprise data modeling tools
- Mandatory (Experience 3) – Must have Deep understanding of dimensional modeling (Kimball / Inmon methodologies), normalization techniques, and schema design for modern data warehouse environments.
- Mandatory (Experience 4) – Proven experience designing data models for modern data platforms such as Snowflake, Databricks, Redshift, Dremio, or similar cloud data warehouse / lakehouse systems.
- Mandatory (Experience 5) – Must have strong SQL expertise and schema design skills, with the ability to validate data model implementations and collaborate closely with data engineering teams
- Mandatory (Education) – Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related technical field.
- Preferred (Experience 1) – Should have familiarity with data governance, metadata management, lineage, and business glossary tools such as Collibra, Alation, or Microsoft Purview.
- Preferred (Experience 2) – Exposure to data integration pipelines and ETL frameworks such as Informatica, DBT, Airflow, or similar tools.
- Preferred (Data Management) – Understanding of master data management (MDM) and reference data management principles.
- Preferred (Domain) – Experience working with high-tech or manufacturing data domains, including customer, product, or supply chain data models
Shubham Vishwakarma
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