{"id":3216978,"url":"https://alion.io/job/capgemini-senior-data-extraction-engineer","title":"Senior Data Extraction Engineer","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":80,"open_postings":1791,"ghost_share":0.001,"stale_share":0.793,"repost_share":0.009,"time_to_fill_p50_days":28,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Aguascalientes, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":35000,"max_usd":91000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2491},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"IAM","optional":false},{"name":"LLM","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Unstructured.io","optional":false},{"name":"Vertex AI","optional":false},{"name":"AI Agents","optional":true},{"name":"CI/CD","optional":true},{"name":"dbt","optional":true},{"name":"Git","optional":true},{"name":"Least Privilege","optional":true},{"name":"OCR","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-24T00:43:17Z","employer_posted_date":"2026-10-24","last_verified_at":"2026-10-25T22:24:39Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"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.\nYour Role\nAs 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).\nYou 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.\nThis role requires a builder mentality, strong cloud expertise, and a passion for transforming both structured and unstructured data into valuable business assets.\nResponsibilities & Scope\nCloud Data Architecture & Platform Design\nDesign end-to-end data platform architectures supporting data ingestion, transformation, storage, and consumption.\nDefine scalable, maintainable, and cost-effective cloud data solutions leveraging Google Cloud Platform services.\nEstablish reusable architecture patterns, standards, and best practices across the data ecosystem.\nEvaluate and select appropriate technologies and architectural approaches for:\nBatch Processing\nStream Processing\nEvent-Driven Architectures\nStructured Data Workloads\nUnstructured Data Workloads\nTranslate business and operational requirements into scalable technical solutions.\nData Pipeline Engineering\nDesign, develop, deploy, and support enterprise-grade data pipelines.\nBuild ingestion frameworks for enterprise source systems including:\nOracle\nCRM Platforms\nInternal Business Applications\nEngineering Systems\nDevelop ELT and ETL processes that support enterprise analytics and operational data requirements.\nImplement data quality validation, reconciliation, and monitoring capabilities.\nSupport production operations and continuously optimize pipeline performance.\nGoogle Cloud Platform Engineering\nDesign and implement solutions using:\nBigQuery\nCloud Storage (GCS)\nDataflow\nApache Beam\nPub/Sub\nCloud Composer (Airflow)\nCloud Functions\nCloud Run\nVertex AI\nManage data storage architecture, lifecycle policies, security controls, and cloud resource optimization.\nImplement Infrastructure-as-Code (IaC) and deployment automation practices.\nContribute to cloud governance, reliability, scalability, and operational excellence.\nAI & Unstructured Data Processing\nDevelop processing pipelines for unstructured and multimodal data including:\nPDF Documents\nExcel Files\nPowerPoint Presentations\nAudio Recordings\nVideos\nEngineering Drawings\nEnterprise Documentation\nLeverage Google AI services such as:\nDocument AI\nVision AI\nSpeech-to-Text\nVideo Intelligence API\nVertex AI\nBuild intelligent data extraction workflows that convert unstructured content into trusted structured datasets.\nSupport AI and GenAI initiatives through scalable data preparation and orchestration frameworks.\nLLM & Data Product Enablement\nImplement Large Language Model (LLM)-based solutions to:\nClassify content\nExtract information\nSummarize documents\nGenerate structured output\nBuild reusable data products that support AI, analytics, and reporting use cases.\nDevelop semantic and business-aligned data models that improve accessibility and usability across business domains.\nData Modeling & Common Information Models\nDesign and maintain Common Information Models (CIM) that align technical data structures with business concepts.\nSupport enterprise semantic layers that enable consistent interpretation of data across multiple business functions.\nImplement:\nDimensional Models\nMedallion Architecture (Bronze, Silver, Gold)\nData Products\nBusiness Semantic Layers\nSupport downstream BI, analytics, AI, and operational reporting solutions.\nSecurity, Governance & Reliability\nImplement enterprise security best practices across cloud environments.\nConfigure and maintain:\nIAM Policies\nService Accounts\nRole Management\nWorkload Identity\nAccess Controls\nEstablish observability frameworks including:\nLogging\nMonitoring\nAlerting\nSLA Tracking\nData Quality Monitoring\nEnsure compliance with governance, security, and operational standards.\nTechnical Leadership & Collaboration\nProvide technical leadership and mentorship to engineering teams.\nCreate technical designs, architecture documentation, and implementation standards.\nCollaborate with Product Managers, Business Stakeholders, Architects, and Data Consumers.\nParticipate in architecture reviews, design workshops, and strategic data initiatives.\nPromote engineering excellence and continuous improvement within the Data Engineering organization.\nYour Profile\nArchitect and builder mindset - Equally comfortable defining architectures and writing production code.\nCloud-native engineer - Passionate about building scalable and resilient platforms on Google Cloud.\nProblem solver - Skilled at designing solutions for complex enterprise data challenges.\nInnovation-driven professional - Interested in AI, GenAI, analytics, and modern data architectures.\nCollaborative leader - Enjoys partnering with business and technical teams to deliver impactful solutions.\nContinuous learner - Keeps current with evolving cloud, AI, and data engineering technologies.\nQualifications & Preferred Skills\nRequired Skills\nGoogle Cloud Platform\nStrong hands-on experience with:\nBigQuery\nGoogle Cloud Storage (GCS)\nDataflow\nApache Beam\nCloud Composer (Airflow)\nPub/Sub\nCloud Run\nCloud Functions\nVertex AI\nCloud IAM\nData Engineering\nAdvanced Python development skills.\nStrong SQL expertise, including BigQuery SQL.\nExperience building production-grade ETL/ELT pipelines.\nData modeling and dimensional modeling expertise.\nExperience with dbt and data transformation frameworks.\nStrong understanding of data quality, reliability, and observability practices.\nAI & Unstructured Data Processing\nExperience working with:\nGoogle Document AI\nOCR technologies\nVision AI\nSpeech-to-Text\nVideo Intelligence API\nLLM-based data extraction\nUnstructured document processing\nCloud Security & Governance\nIAM architecture and administration.\nService accounts and workload identities.\nLeast-privilege security design.\nStorage security and data governance.\nAudit logging and compliance practices.\nDevOps & Automation\nTerraform and Infrastructure-as-Code.\nGit-based development workflows.\nCI/CD pipelines.\nAutomated testing and deployment practices.\nPreferred Skills\nExperience implementing Common Information Models (CIM).\nKnowledge of IEC 61968 / IEC 61970 standards.\nExperience within Energy, Utilities, Infrastructure, or Engineering domains.\nExperience designing Data Products and Data Mesh architectures.\nExposure to Generative AI, RAG architectures, and Agentic AI solutions.\nExperience supporting enterprise modernization and cloud transformation programs.\nWhat You'll Love\nOpportunity to build enterprise-scale cloud data platforms from the ground up.\nExposure to cutting-edge technologies across GCP, AI, GenAI, and advanced analytics.\nCollaboration with global teams specializing in Data Engineering, AI, Cloud, and Digital Transformation.\nAccess to certifications, advanced technical training, and continuous learning opportunities.\nCareer growth opportunities within Capgemini's global Data & AI practice.\nInnovative and collaborative work environment focused on engineering excellence.\nNeed to Know\nCareer Development in Spanish & English: Access to cloud certifications, technical academies, mentorship programs, and learning paths focused on Google Cloud, Data Engineering, Analytics, and Artificial Intelligence.\nCommunity Engagement: Participate in innovation programs, technical communities, hackathons, knowledge-sharing initiatives, and networking opportunities across Capgemini.\nAbout Capgemini\nCapgemini is a global business and technology transformation partner, helping organizations accelerate their dual transition to a digital and sustainable world while creating tangible impact for enterprises and society. It is a responsible and diverse organization with more than 340,000 team members in over 50 countries.\nWith a strong heritage of more than 55 years, Capgemini helps clients unlock the value of technology through end-to-end services and solutions, leveraging capabilities in strategy and design, engineering, AI, generative AI, cloud, and data, combined with deep industry expertise and a strong partner ecosystem.\nAt Capgemini Mexico, we are committed to fostering a diverse and inclusive workplace where everyone has equal opportunities. 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