Data Modeler

Location: Bengaluru

Company Description
AuxoAI, named after the Greek goddess of growth, is dedicated to transforming businesses through the power of AI, Data, and Digital technologies. With a mission of "AI Made Real," AuxoAI drives enterprise growth and operational efficiency by reimagining workflows, modernizing infrastructure, and reducing organizational inefficiencies. Our innovative platform, Damia – Enterprise AI Studio, accelerates time-to-value with tailored AI solutions. Focused on aligning technology with business strategies, AuxoAI empowers enterprises to achieve true velocity and sustainable success.

Role Description
AuxoAI 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.

Location   - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)


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 life cycles, 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.

Qualifications
Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
8+ years in data modeling or enterprise data architecture.
Strong experience with modeling tools such as Erwin, ER/Studio, Power Designer, or SQLDBM.
Deep understanding of dimensional modeling (Kimball/Inmon), normalization, and schema design for modern warehouses.
Experience designing models for Snowflake, Dremio, Redshift, or Databricks environments.
Strong SQL and schema design skills, with ability to validate model implementations.
Familiarity with data governance, metadata management, and business glossary tools (e.g., Collibra, Alation, Purview).
Excellent communication skills — able to work with both technical and non-technical stakeholders.

Preferred:
Experience in high-tech or manufacturing data domains (Customer, Product, Supply Chain).
Familiarity with Medallion or Data Vault architectures and how they translate into dimensional models.
Exposure to data integration pipelines and ETL frameworks (Informatica, DBT, Airflow).
Understanding of master data and reference data management principles.