Data Modeller

Capgemini Invent; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Capgemini Invent seeks a Senior Data Modeller / Data Engineer to design enterprise-grade data models and scalable pipelines across cloud and on‑prem environments. You will specialize in conceptual, logical, and physical modelling and semantic ontologies to support analytics, AI/ML, and operational workflows.

Collaborating with architects and SMEs, you will translate complex requirements into robust data structures, leveraging SQL, Spark, and Databricks across AWS/Azure/GCP and modern data

Qualifications

• 6 to 12 years enterprise data architecture experience.
• Semantic/knowledge graph modelling and ontology definitions.
• Large-scale analytical modelling for BI, AI/ML, and reporting ecosystems.
• Designing data pipelines supporting batch and streaming workloads.
• Performance tuning across SQL, MPP, and distributed storage systems.

Responsibilities

• Develop semantic and ontology-driven models for business domains and analytics consumption layers.
• Design data models for Data Warehouses, Big Data ecosystems, Lakehouse architectures, and Graph DB environments.
• Collaborate with solution architects and business SMEs to gather requirements and convert them into scalable data structures.
• Define, implement, and maintain modelling standards, best practices, and governance frameworks.
• Work with data engineering teams to implement models using ETL/ELT pipelines on cloud and on-prem platforms.
• Optimize database performance, indexing, and storage strategies across SQL and MPP systems.
• Contribute to architectural decisions on data ingestion, transformation, lineage, cataloging, and metadata management.
• Ensure compliance with security, privacy, and data quality requirements.
• Mentor junior modelers/engineers and participate in design reviews and architecture forums.
• Semantic modelling, ontology development (OWL/RDF), knowledge graph modelling.
• Normalized and denormalized modelling for operational and analytical systems.
• Modelling for Graph DBs (e.g., Neo4j, Amazon Neptune).
• Experience in working with Databricks
• Strong SQL (mandatory).
• Cloud data warehouses: Redshift, BigQuery, Snowflake (good to have).
• Distributed systems & big data stores: Hadoop ecosystem, Hive, HBase, Delta Lake, Iceberg.
• Cloud platforms: AWS / Azure / GCP (any is acceptable).
• Spark / PySpark (good to have).
• ER/Studio, Erwin, PowerDesigner, dbt, semantic layer tools (e.g., AtScale, LookML, Azure Purview, Data Catalog tools).

Skills

Strong SQL

Data modelling

Analytical thinking

Tools

Databricks

ER/Studio

Erwin

PowerDesigner

dbt

Azure Purview

Data Catalog tools

LookML

Job description

As a Senior Data Modeller / Data Engineer, you will be responsible for designing and implementing enterprise‑grade data models and scalable data engineering solutions across cloud and on‑premise ecosystems. The role requires deep expertise in conceptual, logical, and physical data modelling, semantic/ontology-based modelling, and modern data architecture patterns used in Data Warehouses, Data Lakes, Lakehouses, Big Data platforms, and Graph Databases.

You will work closely with business stakeholders, architects, and engineering teams to translate complex business requirements into robust data structures that support analytics, reporting, AI/ML, and operational workflows. Expertise in SQL, distributed data systems, and cloud platforms (AWS/Azure/GCP) is essential. Experience with Spark/PySpark and modern metadata/semantic modelling frameworks is a strong advantage.

Key Responsibilities

• Develop semantic and ontology‑driven models for business domains and analytics consumption layers.
• Design data models for Data Warehouses, Big Data ecosystems, Lakehouse architectures, and Graph DB environments.
• Collaborate with solution architects and business SMEs to gather requirements and convert them into scalable data structures.
• Define, implement, and maintain modelling standards, best practices, and governance frameworks.
• Work with data engineering teams to implement models using ETL/ELT pipelines on cloud and on‑prem platforms.
• Optimize database performance, indexing, and storage strategies across SQL and MPP systems.
• Contribute to architectural decisions on data ingestion, transformation, lineage, cataloging, and metadata management.
• Ensure compliance with security, privacy, and data quality requirements.
• Mentor junior modelers/engineers and participate in design reviews and architecture forums.
• Semantic modelling, ontology development (OWL/RDF), knowledge graph modelling.
• Normalized and denormalized modelling for operational and analytical systems.
• Modelling for Graph DBs (e.g., Neo4j, Amazon Neptune).
• Experience in working with Databricks
• Strong SQL (mandatory).
• Cloud data warehouses: Redshift, BigQuery, Snowflake (good to have).
• Distributed systems & big data stores: Hadoop ecosystem, Hive, HBase, Delta Lake, Iceberg.
• Cloud platforms: AWS / Azure / GCP (any is acceptable).
• Spark / PySpark (good to have).
• ER/Studio, Erwin, PowerDesigner, dbt, semantic layer tools (e.g., AtScale, LookML, Azure Purview, Data Catalog tools).

Your Qualifications

• Experience – 6 to 12 Years
• Enterprise data architecture and modelling standards.
• Semantic/knowledge graph modelling and business ontology definitions.
• Large‑scale analytical modelling for BI, AI/ML, and reporting ecosystems.
• Designing data pipelines supporting batch and streaming workloads.
• Performance tuning across SQL, MPP, and distributed storage systems.