Senior Data Modeler – AI & Advanced Analytics

Luxoft; India; On-site; INR 2,400,000 - 5,200,000; Full time

Job summary

Luxoft is seeking an experienced Data Modeler with AI expertise to design, develop, and govern enterprise-scale data models for AI-driven analytics, ML, GenAI, and BI initiatives. The role involves conceptual, logical, and physical modeling, data architecture, and governance across cloud data platforms.

You will collaborate 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.

Qualifications

• 10+ years of experience in Data Modeling, Data Architecture, Data Warehousing, and Enterprise Data Management.
• Proven expertise designing conceptual, logical, and physical data models for large-scale platforms.
• Strong experience with Data Vault 2.0, Dimensional Modeling, Kimball, Star/Snowflake, and 3NF modeling.
• Hands-on with ERwin, ER/Studio, PowerDesigner, or SQL Developer Data Modeler.
• Experience with Snowflake, Azure Synapse, Microsoft Fabric, Databricks, SQL Server, Oracle, PostgreSQL, MongoDB, Cassandra.
• Strong AI/ML data foundations including data prep for ML, GenAI, RAG architectures, Knowledge Graphs, Vector Databases, and data governance.
• Cloud tech exposure: Azure, Azure Data Factory, Azure Databricks, Azure OpenAI, AWS, GCP.
• Proficient in SQL, Python, PySpark, and Spark SQL for modeling and analytics.
• Knowledge of data governance, metadata, lineage, MDM, data quality, security, and compliance.
• Excellent stakeholder management and ability to translate requirements into scalable data models.

Responsibilities

• Data Modeling & Architecture: design conceptual, logical, and physical models for enterprise apps.
• Develop dimensional models (star/snowflake) and normalize data structures.
• Create AI-ready models for ML and GenAI use cases and establish data standards and governance.
• AI & Advanced Analytics Enablement: optimize data for AI/ML/NLP/GenAI workloads and support feature engineering datasets.
• Cloud & Data Platform Engineering: implement models on cloud platforms and support data lakehouse and warehouse architectures.
• Governance & Quality: define data quality rules, MDM strategies, and ensure security, privacy, and regulatory compliance.

Skills

Data Modeling

Data Architecture

Data Warehousing

Dimensional Modeling

Data Vault 2.0

Kimball Methodology

Star/Snowflake Schemas

3NF Modeling

SQL

Python

PySpark

Spark SQL

Cloud Data Platforms

Education

Bachelor's degree in Computer Science / IT

Master's degree preferred

Tools

ERwin Data Modeler

ER/Studio

PowerDesigner

SQL Developer Data Modeler

Snowflake

Azure Synapse Analytics

Databricks

MongoDB

Job description

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