Project description

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.

Develop and maintain dimensional models, star schemas, snowflake schemas, and normalized data structures.

Create AI-ready data models supporting machine learning and GenAI use cases.

Define data standards, metadata management, data lineage, and governance processes.

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.

Support feature engineering frameworks and model training datasets.

Develop semantic data models for Knowledge Graphs, Vector Databases, and RAG architectures.

Work with AI Engineers to prepare and optimize data for LLM-based solutions.

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.

Support modern data platforms including Data Lakes, Lakehouse, and Data Warehouse architectures.

Design scalable data structures for structured, semi-structured, and unstructured data.

Optimize data storage, partitioning, and retrieval strategies.

Governance & Quality:

Establish data quality rules and validation frameworks.

Drive master data management (MDM) and reference data strategies.

Ensure compliance with security, privacy, and regulatory standards.

Conduct model reviews and architecture governance assessments.

Skills

Must have

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.

Nice to have

N/A

Other

Languages

English: B2 Upper Intermediate

Seniority

Senior

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