Senior Data Modeler AI & Advanced Analytics

We are seeking an experienced Data Modeler with AI expertise to design, develop, and govern enterprise-scale data models that support advanced analytics, Machine Learning (ML), Generative AI (GenAI), and business intelligence initiatives. You need to have strong experience in conceptual, logical, and physical data modeling, data architecture, cloud data platforms, and AI-ready data ecosystems. You will work closely with Data Architects, Data Engineers, AI/ML Engineers, Business Analysts, and Product Owners to build scalable and high-quality data foundations for AI-driven solutions.

Responsibilities

• Data Modeling Architecture: Design conceptual, logical, and physical data models for enterprise applications.
• Data Modeling Architecture: Develop and maintain dimensional models, star schemas, snowflake schemas, and normalized data structures.
• Data Modeling Architecture: Create AI-ready data models supporting machine learning and GenAI use cases.
• Data Modeling Architecture: Define data standards, metadata management, data lineage, and governance processes.
• Data Modeling Architecture: Collaborate with Data Architects to align models with enterprise architecture standards.
• AI Advanced Analytics Enablement: Design data structures optimized for AI, ML, NLP, and GenAI workloads.
• AI Advanced Analytics Enablement: Support feature engineering frameworks and model training datasets.
• AI Advanced Analytics Enablement: Develop semantic data models for Knowledge Graphs, Vector Databases, and RAG architectures.
• AI Advanced Analytics Enablement: Work with AI Engineers to prepare and optimize data for LLM-based solutions.
• AI Advanced Analytics Enablement: Ensure datasets meet quality, consistency, explainability, and regulatory requirements.
• Cloud Data Platform Engineering: Partner with Data Engineering teams to implement data models on cloud platforms.
• Cloud Data Platform Engineering: Support modern data platforms including Data Lakes, Lakehouse, and Data Warehouse architectures.
• Cloud Data Platform Engineering: Design scalable data structures for structured, semi-structured, and unstructured data.
• Cloud Data Platform Engineering: Optimize data storage, partitioning, and retrieval strategies.
• Governance Quality: Establish data quality rules and validation frameworks.
• Governance Quality: Drive master data management (MDM) and reference data strategies.
• Governance Quality: Ensure compliance with security, privacy, and regulatory standards.
• Governance Quality: Conduct model reviews and architecture governance assessments.

Skills

• 10+ years of overall experience in Data Modeling, Data Architecture, Data Warehousing, and Enterprise Data Management.
• Proven expertise in designing conceptual, logical, and physical data models for large-scale enterprise platforms.
• Strong experience with Data Vault 2.0, Dimensional Modeling, Kimball Methodology, Star/Snowflake Schemas, and 3NF Modeling.
• Hands-on experience with industry-standard modeling tools such as ERwin Data Modeler, ER/Studio, PowerDesigner, or SQL Developer Data Modeler.
• Experience working with modern data platforms including Snowflake, Azure Synapse Analytics, Microsoft Fabric, Databricks, SQL Server, Oracle, PostgreSQL, MongoDB, and Cassandra.
• Strong understanding of AI/ML data foundations, including data preparation for Machine Learning, Generative AI, RAG architectures, Knowledge Graphs, Vector Databases, Feature Engineering, and AI Data Governance.
• Experience designing AI-ready data ecosystems that support advanced analytics, predictive modeling, and LLM-based solutions.
• Hands-on experience with cloud technologies such as Microsoft Azure, Azure Data Factory, Azure Databricks, Azure OpenAI, AWS, and GCP.
• Proficiency in SQL, Python, PySpark, and Spark SQL for data modeling, transformation, and analytics workloads.
• Strong knowledge of data governance, metadata management, data lineage, master data management (MDM), data quality frameworks, security, and regulatory compliance.
• Experience collaborating with Data Architects, Data Engineers, AI/ML Engineers, Business Analysts, and Product Owners in enterprise environments.
• Excellent analytical, communication, and stakeholder management skills with the ability to translate business requirements into scalable data solutions.