Senior Data Extraction Engineer

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Your Role As a Senior Data Engineer – Google Cloud Platform at Capgemini , you will play a key role in building and evolving enterprise-scale data platforms that power analytics, reporting, AI, and business-critical data products. You will combine strategic architecture thinking with hands-on engineering expertise to design, build, deploy, and support production-grade data solutions on Google Cloud Platform (GCP). You will own data pipelines end-to-end, from initial architecture and design through implementation, deployment, monitoring, and operational support. Working closely with Product Owners, Data Architects, Business Stakeholders, and Engineering teams, you will help establish scalable data foundations that enable trusted and accessible enterprise data. This role requires a builder mentality, strong cloud expertise, and a passion for transforming both structured and unstructured data into valuable business assets. Responsibilities & Scope Cloud Data Architecture & Platform Design • Design end-to-end data platform architectures supporting data ingestion, transformation, storage, and consumption. • Define scalable, maintainable, and cost-effective cloud data solutions leveraging Google Cloud Platform services. • Establish reusable architecture patterns, standards, and best practices across the data ecosystem. • Evaluate and select appropriate technologies and architectural approaches for: • Batch Processing • Stream Processing • Event-Driven Architectures • Structured Data Workloads • Unstructured Data Workloads • Translate business and operational requirements into scalable technical solutions. Data Pipeline Engineering • Design, develop, deploy, and support enterprise-grade data pipelines. • Build ingestion frameworks for enterprise source systems including: • Oracle • CRM Platforms • Internal Business Applications • Engineering Systems • Develop ELT and ETL processes that support enterprise analytics and operational data requirements. • Implement data quality validation, reconciliation, and monitoring capabilities. • Support production operations and continuously optimize pipeline performance. Google Cloud Platform Engineering • Design and implement solutions using: • BigQuery • Cloud Storage (GCS) • Dataflow • Apache Beam • Pub/Sub • Cloud Composer (Airflow) • Cloud Functions • Cloud Run • Vertex AI • Manage data storage architecture, lifecycle policies, security controls, and cloud resource optimization. • Implement Infrastructure-as-Code (IaC) and deployment automation practices. • Contribute to cloud governance, reliability, scalability, and operational excellence. AI & Unstructured Data Processing • Develop processing pipelines for unstructured and multimodal data including: • PDF Documents • Excel Files • PowerPoint Presentations • Audio Recordings • Videos • Engineering Drawings • Enterprise Documentation • Leverage Google AI services such as: • Document AI • Vision AI • Speech-to-Text • Video Intelligence API • Vertex AI • Build intelligent data extraction workflows that convert unstructured content into trusted structured datasets. • Support AI and GenAI initiatives through scalable data preparation and orchestration frameworks. LLM & Data Product Enablement • Implement Large Language Model (LLM)-based solutions to: • Classify content • Extract information • Summarize documents • Generate structured output • Build reusable data products that support AI, analytics, and reporting use cases. • Develop semantic and business-aligned data models that improve accessibility and usability across business domains. Data Modeling & Common Information Models • Design and maintain Common Information Models (CIM) that align technical data structures with business concepts. • Support enterprise semantic layers that enable consistent interpretation of data across multiple business functions. • Implement: • Dimensional Models • Medallion Architecture (Bronze, Silver, Gold) • Data Products • Business Semantic Layers • Support downstream BI, analytics, AI, and operational reporting solutions. Security, Governance & Reliability • Implement enterprise security best practices across cloud environments. • Configure and maintain: • IAM Policies • Service Accounts • Role Management • Workload Identity • Access Controls • Establish observability frameworks including: • Logging • Monitoring • Alerting • SLA Tracking • Data Quality Monitoring • Ensure compliance with governance, security, and operational standards. Technical Leadership & Collaboration • Provide technical leadership and mentorship to engineering teams. • Create technical designs, architecture documentation, and implementation standards. • Collaborate with Product Managers, Business Stakeholders, Architects, and Data Consumers. • Participate in architecture reviews, design workshops, and strategic data initiatives. • Promote engineering excellence and continuous improvement within the Data Engineering organization. Your Profile • Architect and builder mindset – Equally comfortable defining architectures and writing production code. • Cloud-native engineer – Passionate about building scalable and resilient platforms on Google Cloud. • Problem solver – Skilled at designing solutions for complex enterprise data challenges. • Innovation-driven professional – Interested in AI, GenAI, analytics, and modern data architectures. • Collaborative leader – Enjoys partnering with business and technical teams to deliver impactful solutions. • Continuous learner – Keeps current with evolving cloud, AI, and data engineerin