Data Modeling engineers (Gurugram)

Role Overview We are looking for an experienced Data Modeling Engineer to design, develop, and maintain scalable enterprise data models that support analytics, reporting, AI, and machine learning initiatives. The ideal candidate will have strong expertise in conceptual, logical, and physical data modeling, SQL, metadata management, data governance, and AI-ready data architecture. This role will collaborate closely with Data Engineers, AI Engineers, Architects, and Business stakeholders to build high-quality, governed data assets for enterprise applications and AI-powered solutions. Key Responsibilities - Design and maintain conceptual, logical, and physical data models for enterprise applications and data platforms. - Develop scalable database schemas, relationships, indexes, and optimized SQL queries. - Create AI-ready data models to support LLMs, RAG pipelines, semantic search, and AI applications. - Design metadata structures for document processing, embeddings, and vector databases. - Develop data dictionaries, business glossaries, and metadata documentation. - Implement data quality rules, validation logic, and reconciliation processes. - Define and maintain data lineage, governance, and master data management standards. - Collaborate with Data Engineers to support ETL/ELT pipelines, data lakes, and data warehouses. - Design and optimize relational and dimensional data models for reporting and analytics. - Work with AI Engineers to structure data for Retrieval-Augmented Generation (RAG) and Knowledge Graph implementations. - Ensure compliance with enterprise data architecture, governance, and security standards. - Create ER diagrams, technical documentation, and knowledge transfer materials. Required Qualifications - Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related field. - Robust experience in data modeling using conceptual, logical, and physical modeling techniques. - Expertise in SQL and relational database design. - Experience with enterprise databases such as SQL Server, Oracle, PostgreSQL, MySQL, or Snowflake. - Hands-on experience with data warehousing, dimensional modeling (Star/Snowflake schema), and data lakes. - Experience with metadata management, data lineage, data quality, and data governance. - Understanding of AI-ready data design, embeddings, semantic search, and RAG architectures. - Knowledge of vector databases such as Pinecone, FAISS, Weaviate, Milvus, OpenSearch, or pgvector. - Experience with ER modeling tools such as Erwin, ER/Studio, Visio, or Lucidchart. - Strong analytical, problem-solving, and documentation skills. Preferred Qualifications - Experience in financial services, banking, healthcare, or other regulated industries. - Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). - Experience with Knowledge Graphs, ontologies, and semantic data modeling. - Knowledge of modern data catalog and governance tools such as Microsoft Purview, Collibra, Alation, or Informatica. - Experience supporting AI/ML initiatives with structured and unstructured enterprise data. Key Skills - Data Modeling - Conceptual, Logical & Physical Data Models - SQL - Relational Databases - Data Warehousing - Data Lakes - Enterprise Data Architecture - Metadata Management - Data Governance - Data Quality - Data Lineage - ER Modeling - AI-Ready Data - RAG Data Design - Semantic Search - Knowledge Graphs - Vector Databases (Pinecone, FAISS, Weaviate, Milvus, pgvector, OpenSearch) - Snowflake, SQL Server, Oracle, PostgreSQL, MySQL - Cloud (AWS, Azure, GCP) .