Data Engineer

Capgemini; Estado de México; On-site; MXN 900,000 - 1,300,000; Full time

Benefits offered by this job

Certifications

Leadership programs

Career growth

Job summary

Capgemini Mexico seeks a Lead Data Platform Architect to define the enterprise data platform strategy, architecture standards, and modernization roadmap. You will design scalable, cloud-native data ecosystems that support analytics, data products, AI initiatives, and enterprise integration.

This role combines strategic leadership with deep technical expertise across data engineering, governance, and AI enablement.

Qualifications

• Bachelor's degree in Computer Science, Information Systems or related field.
• 8+ years of experience in AI enablement and enterprise-scale data ecosystems.
• Extensive experience designing enterprise data platforms and integration architectures.
• Strong architecture governance and standards development experience.

Responsibilities

• Define target-state architecture, reference architectures, standards, and engineering principles for enterprise data platforms.
• Lead platform modernization, cloud adoption, and tech transformation initiatives.
• Design patterns for data ingestion, ETL/ELT, batch and real-time processing, and data warehousing.

Skills

Enterprise data platforms

Data governance

AI enablement

Architecture governance

SQL

Python

BigQuery scripting

Education

Bachelor's Degree in Computer Science/Information Systems or related field

Tools

dbt Core

Cloud Composer (Airflow)

Cube Cloud

OpenMetadata

Kafka

Job 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.

Your Role

As a Lead Data Platform Architect at Capgemini, you will be responsible for defining and governing the enterprise data platform strategy, architecture standards, and modernization roadmap. You will drive the design of scalable, secure, resilient, and cloud-native data ecosystems that support analytics, data products, reporting, AI initiatives, and enterprise integration capabilities.

This role combines strategic architecture leadership with deep technical expertise across Data Engineering, Platform Engineering, Analytics Engineering, Governance, Metadata Management, and AI Enablement. You will partner with business and technology stakeholders to establish long-term architectural direction while ensuring solutions remain practical, operationally effective, and aligned with modernization goals.

Responsibilities & Scope

Enterprise Data Architecture & Strategy

• Define the target-state architecture, reference architectures, technical standards, and engineering principles for enterprise data platforms.
• Establish architectural patterns that support scalability, security, governance, operational efficiency, and future growth.
• Develop reusable frameworks and best practices that promote consistency across data initiatives.
• Guide enterprise-wide platform modernization and cloud adoption strategies.
• Evaluate emerging technologies and recommend solutions that improve platform capabilities and business value.
• Design scalable architectures supporting:
• Data Ingestion
• ETL/ELT Processing
• Batch Processing
• Real-Time Processing
• Data Warehousing
• Data Products
• Analytics Consumption
• Define architecture standards for cloud-native engineering, CI/CD, infrastructure automation, and platform operations.
• Lead migration planning, platform modernization, and technology transformation initiatives.
• Ensure operational readiness and sustainability across platform implementations.

Integration & Data Ecosystem Design

• Design architecture patterns for enterprise integration across cloud, SaaS, and on-premises platforms.
• Define standards for data movement, orchestration, transformation, and data accessibility.
• Establish scalable approaches for API integrations, event-driven architectures, and distributed data services.
• Support enterprise integration ecosystems spanning multiple cloud platforms and technologies.

Metadata, Governance & Data Quality

• Metadata Management
• Data Cataloging
• Data Governance
• Data Stewardship
• Data Quality
• Establish governance standards and controls that improve trust, transparency, and discoverability of enterprise data assets.
• Ensure alignment with security, audit, compliance, privacy, and regulatory requirements.
• Promote enterprise-wide adoption of governed and trusted data practices.
• Define architecture patterns supporting AI-enabled capabilities, including:
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• AI-Assisted Analytics
• Agent-Based Architectures
• Knowledge Retrieval Frameworks
• Collaborate with Data Scientists and AI Engineering teams to build scalable AI-ready platforms.
• Support enterprise adoption of Generative AI and advanced analytics capabilities.
• Establish standards for AI governance, reliability, and responsible AI practices.

Reliability, Security & Operational Excellence

• Observability
• Monitoring
• Reliability
• Disaster Recovery
• Security
• Cost Optimization
• Performance Management
• Define operational frameworks that improve platform resilience and engineering productivity.
• Ensure enterprise platforms meet security and governance requirements while maintaining efficiency and scalability.
• Provide technical leadership and mentorship across Data Platform, Engineering, Analytics, and Architecture teams.
• Collaborate with Data Product teams, Analytics Engineering teams, business stakeholders, and executive leadership.
• Facilitate architecture reviews, technical governance discussions, and strategic decision-making.
• Promote continuous improvement, innovation, and engineering excellence across the organization.

Your Profile

• Strategic architect – Capable of defining long-term platform visions while supporting practical execution.
• Systems thinker – Understands how data, analytics, integration, AI, and cloud ecosystems operate together.
• Technical leader – Influences architectural direction and promotes engineering best practices.
• Innovation advocate – Passionate about cloud modernization, AI enablement, and next-generation data platforms.
• Collaborative communicator – Bridges business and technology conversations effectively.
• Continuous learner – Stays current with emerging technologies, industry trends, and modern architecture patterns.

Qualifications & Preferred Skills

Required Qualifications

• Bachelor's Degree in:
• Computer Science
• Information Systems
• Or a related technical field.
• 8+ years of experience in:
• AI Enablement
• Proven experience designing and governing enterprise-scale data ecosystems.

Required Skills

• Extensive experience designing enterprise data platforms and integration architectures.
• Deep understanding of data product ecosystems, analytics platforms, and enterprise reporting environments.
• Strong architecture governance and standards development experience.

Experience with modern cloud technologies including:

• Google BigQuery
• Cloud-Native Data Architectures

Strong expertise in:

• SQL
• Python
• BigQuery Scripting
• ETL/ELT Frameworks
• Data Modeling
• Data Orchestration

Experience with:

• dbt Core
• Cloud Composer (Airflow)
• Cube Cloud
• Infrastructure Automation
• DevOps Practices

Governance & Metadata Management

Experience implementing:

• Metadata Management Platforms
• OpenMetadata
• Data Catalog Solutions
• Governance Frameworks
• Data Quality Controls
• Ability to create reusable architecture patterns and engineering standards.
• Strong technical documentation and communication capabilities.
• Experience influencing enterprise technology decisions and guiding engineering teams.

Preferred Skills

Artificial Intelligence & Advanced Analytics

Experience with:

• Generative AI Platforms
• Retrieval-Augmented Generation (RAG)
• Semantic Retrieval
• Agent-Based Architectures
• OpenMetadata
• BigQuery
• dbt
• Kafka
• Composer
• Cube Cloud
• Multi-cloud environments (GCP and Azure)

Industry Experience

• Retail
• Consumer Products
• Digital Commerce
• Customer Data Platforms

What You'll Love

• Opportunity to define enterprise-wide data and AI platform strategies.
• Exposure to cutting-edge cloud, analytics, metadata, and AI technologies.
• Leadership opportunities across large-scale modernization initiatives.
• Collaboration with global Data, AI, Cloud, and Architecture teams.
• Access to advanced certifications, technical academies, and leadership development programs.
• Career growth within Capgemini's Global Data & AI and Cloud practices.

Need to Know

Career Development in Spanish & English: Access to technical certifications, architecture leadership programs, cloud learning paths, mentorship opportunities, and continuous professional development.

Community Engagement: Participate in innovation programs, architecture councils, technical communities, industry events, knowledge-sharing forums, and volunteering initiatives across Capgemini.

About Capgemini

Capgemini 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.

With 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, data, and digital transformation.

At Capgemini Mexico, we are committed to fostering a diverse and inclusive workplace where everyone has equal opportunities. We welcome applications from all qualified candidates and evaluate them based on merit, skills, qualifications, and experience relevant to the role.