Join Capgemini Engineering, a global leader in engineering services, where we empower innovative companies with our digital and software technology expertise.

We are a collaborative community dedicated to shaping the future of technology, from autonomous vehicles to advanced robotics. At Capgemini, you'll find a career brimming with opportunities to make a real difference and experience a dynamic work environment.

Empower your career path with us, supported by colleagues worldwide. We enable leading organizations to harness technology's power, building a more sustainable and inclusive world.

As a Data Engineer, your primary role will be designing, building, and maintaining enterprise data platforms essential for AI-driven products and analytics.

You will collaborate closely with Data Scientists, Architects, AI Engineers, and business stakeholders to create robust data pipelines, digital twin platforms, knowledge graphs, and AI-ready data architectures.

Key responsibilities include developing data connectors, building large-scale data onboarding frameworks, transforming telemetry into incident-aware datasets, and creating Network Digital Twin and topology graph platforms. You will also manage Knowledge Graphs, develop reusable Feature Engineering Frameworks, enable foundations for Generative AI technologies like RAG and Vector Databases, and optimize data pipelines for AI/ML model lifecycle management.

Furthermore, you will implement metadata management, lineage tracking, observability, security, auditing, and governance frameworks, while supporting integrations with AI reasoning engines and agent orchestration platforms.

Your efforts will contribute to delivering scalable, enterprise-grade AI and analytics solutions through cross-functional collaboration.

We are seeking individuals with strong programming proficiency in Python and SQL, complemented by experience in JavaScript, React, or Angular.

A solid understanding of Data Modeling, ETL/ELT processes, and Data Architecture principles is essential, alongside hands-on experience with Apache Spark, Spark Streaming, and Kafka.

Building and managing Data Lakes is a key requirement. Candidates should possess extensive experience with AWS services such as S3, Glue, Lambda, and SageMaker.

Knowledge of Machine Learning, Deep Learning, Predictive Analytics, and Statistical Modeling is crucial. Experience with Generative AI technologies, including LLMs, Prompt Engineering, RAG, AI Agents, and Agentic AI Frameworks, is highly valued.

Expertise in Relational Databases, Graph Databases, and Vector Databases is expected. You should also be skilled in building APIs, Data Pipelines, and Batch/Real-Time Data Processing frameworks.

A deep understanding of Data Quality, Metadata Management, Data Lineage, and Data Governance is required. Familiarity with AWS Glue, Azure Data Factory, SageMaker Pipelines, and Digital Twin platforms is advantageous.

Knowledge of GraphRAG and advanced RAG solutions, coupled with MLOps and LLMOps implementation experience, is desirable. Experience with DataOps practices, automation frameworks, and Enterprise Knowledge Management Platforms, along with strong analytical, debugging, and problem-solving skills, will set you apart.