New opportunity

Data Engineer

Capgemini Engineering · Gurgaon, India

About this role

As a Data Engineer at Capgemini, you will design, build, and maintain enterprise data platforms that support AI-driven products and analytics initiatives. You will collaborate with Data Scientists, Architects, AI Engineers, and business stakeholders to develop robust data pipelines, digital twin platforms, knowledge graphs, and AI-ready data architectures. Responsibilities include designing data source connectors, building ingestion and validation frameworks, developing Network Digital Twin and topology graph platforms, and managing Knowledge Graphs and semantic data models. You will also develop reusable Feature Engineering Frameworks, enable data foundations for RAG and AI Agent frameworks, and implement metadata management, lineage tracking, and governance. Required qualifications include strong programming expertise in Python and SQL, experience with JavaScript/React/Angular, and deep knowledge of Data Modeling, ETL/ELT, and Data Architecture. Hands-on experience with Apache Spark, Kafka, and AWS services (S3, Glue, Lambda, SageMaker) is essential. You should have knowledge of Machine Learning, Deep Learning, and Generative AI technologies including LLMs, Prompt Engineering, and Agentic AI frameworks. Experience with MLOps, LLMOps, and DataOps practices is required. The role offers the opportunity to work on cutting-edge AI and digital twin technologies within a collaborative, innovation-driven environment.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLJavaScriptReactAngularData ModelingETL/ELTData ArchitectureApache SparkSpark StreamingKafkaData LakesAWS S3AWS GlueAWS LambdaAmazon SageMakerMachine LearningDeep LearningPredictive AnalyticsStatistical ModelingGenerative AILarge Language Models (LLMs)Prompt EngineeringRAGAI AgentsGraph DatabasesVector DatabasesAzure Data FactoryMLOpsLLMOps

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Building AI-Ready Data Foundations
The role requires creating robust data pipelines and feature engineering frameworks specifically to support RAG, GraphRAG, and AI Agent architectures. You must prioritize the development of scalable ingestion and validation frameworks that ensure data is ready for advanced machine learning and generative AI applications.
Designing Digital Twin and Knowledge Graph Platforms
A core duty is the design and maintenance of Network Digital Twin and topology graph platforms. This involves managing complex semantic data models, ontologies, and entity relationships to transform raw telemetry data into actionable, incident-aware datasets.
Implementing Data Governance and Observability
You are responsible for the end-to-end lifecycle of data, including metadata management, lineage tracking, and security. You must ensure that all enterprise data platforms adhere to strict governance and observability standards to maintain data quality in large-scale environments.
Cross-Functional Collaboration for AI Solutions
Success depends on working closely with Data Scientists, Architects, and business stakeholders. You must be able to translate complex business requirements into technical data architectures that support AI reasoning engines and recommendation systems.

Likely challenges to think through

Balancing Traditional ETL with Modern AI Workflows
You will likely face the challenge of integrating legacy enterprise data with modern vector and graph databases. Prepare by demonstrating how you manage the trade-off between standard batch processing and the real-time, low-latency requirements of Generative AI and agentic frameworks.
Managing Complexity in Digital Twin Architectures
Developing Network Digital Twins requires deep domain knowledge of topology mapping and telemetry. The challenge lies in maintaining high-fidelity representations of physical systems; prepare by discussing how you handle data consistency and synchronization between physical and virtual environments.
Operationalizing MLOps and LLMOps at Scale
Implementing MLOps and LLMOps involves significant overhead in monitoring and lifecycle management. You may be challenged on how to automate these processes effectively; prepare by focusing on your experience with SageMaker pipelines and automated observability frameworks.

Practice your answers

Technical practice

Designing Data Pipelines for AI/ML

What to coverExplain your approach to building robust data pipelines that support both batch and real-time processing. Discuss how you ensure data quality, lineage, and observability within these pipelines, especially when feeding data into SageMaker or AI agent frameworks.

Knowledge Graphs and Semantic Modeling

What to coverDescribe your experience in building and managing Knowledge Graphs. How do you approach defining ontology frameworks and entity relationships to ensure they are effective for downstream AI reasoning engines or GraphRAG applications?

Feature Engineering and Feature Stores

What to coverHow do you design reusable feature engineering frameworks? Discuss the trade-offs between different feature store architectures and how you maintain consistency between training and inference data for machine learning models.

Implementing RAG and Vector Databases

What to coverWhat are the key technical considerations when building data foundations for Retrieval-Augmented Generation (RAG)? Explain how you select and manage vector databases to support efficient semantic search and retrieval.

Digital Twin and Topology Mapping

What to coverDiscuss your experience with Network Digital Twin platforms. How do you transform raw telemetry data into incident-aware datasets and maintain accurate topology mappings in a graph-based structure?

MLOps and LLMOps Practices

What to coverHow do you implement MLOps and LLMOps to manage the lifecycle of AI models? Focus on your experience with model monitoring, automated deployment, and governance frameworks in a cloud environment like AWS.

Data Ingestion and Validation Frameworks

What to coverExplain how you design data contracts and validation frameworks for large-scale data onboarding. How do you ensure that incoming data from diverse sources meets the requirements for enterprise-grade analytics?

Cloud Architecture with AWS

What to coverDescribe how you leverage AWS services like S3, Glue, and Lambda to build scalable data architectures. Suggestion: Be prepared to discuss how you optimize these services for cost and performance in a high-throughput data environment.

Behavioural practice

Cross-Functional Collaboration

What to coverDescribe a situation where you had to collaborate with Data Scientists or AI Engineers to deliver a complex data solution. How did you align on technical requirements and ensure the data architecture met their specific modeling needs?

Problem-Solving in Data Engineering

What to coverTell me about a time you encountered a significant bottleneck or failure in a data pipeline. What was your process for debugging the issue, and what steps did you take to prevent it from recurring?

Adapting to New AI Technologies

What to coverThe field of Generative AI and Agentic frameworks evolves rapidly. Provide an example of how you have proactively learned and applied a new technology or framework to improve an existing project or process.

Managing Data Governance and Security

What to coverDescribe a scenario where you had to balance the need for data accessibility with strict governance and security requirements. How did you implement metadata management or lineage tracking to satisfy these constraints?

Sharpen your knowledge

Topics to prepare

AI-Ready Data Architecture
Review the principles of designing data platforms that support RAG, GraphRAG, and Agentic AI frameworks. Exercise: Sketch an architecture diagram that connects raw telemetry data to a vector database and a knowledge graph, ensuring metadata lineage and governance are integrated at each stage.
Knowledge Graphs and Semantic Modeling
Understand the role of ontologies and entity relationships in building digital twins. Exercise: Define a simple schema for a network topology graph, identifying the nodes and edges required to represent a system's state and how this semantic model supports downstream AI reasoning.
MLOps and LLMOps Lifecycle
Study the operational requirements for deploying and monitoring models in production. Exercise: Outline a CI/CD pipeline for an LLM application, focusing on how to automate data validation, model performance monitoring, and prompt versioning.

Skills to revise

Apache Spark and Kafka Integration
Practice building robust data pipelines for real-time and batch processing. Exercise: Develop a Python script using PySpark to ingest a stream of JSON data from Kafka, perform a transformation, and write the output to an S3 data lake with schema validation.
Vector Database Implementation
Gain hands-on experience with vector databases to support RAG workflows. Exercise: Create a small-scale prototype that embeds text documents using a pre-trained model, stores them in a vector database, and performs a similarity search based on a user query.
AWS Data Services
Master the orchestration of AWS Glue, Lambda, and SageMaker. Exercise: Configure an AWS Glue job to perform ETL on raw data stored in S3, and trigger a SageMaker pipeline to retrain a model based on the processed data.

Your preparation checklist

Prepare a technical walkthrough of an AI-ready data pipeline
Develop a concise presentation or code sample demonstrating an end-to-end pipeline you have built using Python, Apache Spark, and Kafka. Highlight how you handled data ingestion, validation, and schema evolution, specifically focusing on how the data was prepared for downstream AI or ML model consumption.
Demonstrate expertise in Graph and Vector database integration
Prepare to discuss your experience with Knowledge Graphs or Vector databases. Be ready to explain how you have implemented semantic data models or enabled RAG (Retrieval-Augmented Generation) frameworks, as these are core requirements for the role's focus on AI-driven platforms.
Review MLOps and LLMOps lifecycle management
Be prepared to explain your approach to managing the full lifecycle of AI models, including training, deployment, and monitoring. Specifically, articulate how you have implemented metadata management, data lineage, and observability to ensure governance and quality in production environments.
Articulate your experience with Digital Twin architectures
Prepare a brief explanation of a project where you transformed raw telemetry data into a digital twin or topology graph. Focus on the data modeling challenges you faced and how you ensured the platform remained scalable and incident-aware.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding the team's current stack, such as: 'How does the team balance the development of reusable feature engineering frameworks with the specific requirements of individual AI projects?' or 'What are the primary challenges the team faces when integrating GraphRAG with existing enterprise data platforms?'

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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