Data Modeler

Job 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 .   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.   Requirements 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,  PowerDesigner , 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.   Requirements Bachelor’s degree in Computer Science, Engineering, or related field; or equivalent work experience. Proven experience as a DevOps Engineer or similar role with hands-on expertise in AWS and Azure cloud environments. Strong proficiency in Azure DevOps, Git, GitHub, Jenkins, and CI/CD pipeline automation. Experience deploying and managing Kubernetes clusters (EKS, AKS) and container orchestration platforms. Deep understanding of cloud-native architectures, microservices, and serverless computing. Familiarity with Azure Synapse, ADF, ADLS, and AWS data services (EMR, Redshift, Glue) for data integration and analytics. Solid grasp of infrastructure as code (IaC) tools like Terraform, CloudFormation, or ARM templates. Experience with monitoring tools (e.g., Prometheus, Grafana) and logging solutions for cloud-based applications. Excellent troubleshooting skills and ability to resolve complex technical issues in production environments.