Senior Forward Deployed Engineer responsible for designing, building, and deploying data cloud and machine learning solutions on Google Cloud for customers.
Responsibilities
Collaborate with customers to build and release production data and ML workloads
Write, test, and tune PySpark, SQL, and Python code on BigQuery, Managed Spark, Spanner, and AlloyDB
Create proof‑of‑concepts using confidential data with anonymization
Build agent‑based data foundations for lakehouse architecture, metadata, semantic context, and custom agent access control
Train, evaluate, and deploy machine learning and agent components, and monitor performance
Requirements
Bachelor's degree in Engineering, Computer Science, or related field
Global GTM Practice Architect for Agentic Data Cloud within Google Cloud, responsible for owning global strategy and business goals, driving GTM motion, and partnering with cross‑functional teams.
We are looking for a hands-on Engineering Manager to lead our DevOps function across two specialized engineering teams: Site Reliability Engineering (SRE) and Platform Engineering.
Responsibilities
Own the DevOps roadmap and delivery outcomes
Drive predictable execution through planning and execution practices
Lead SRE and Platform Engineering teams
Own people management including performance reviews, career development, hiring, and structure
Partner with engineering teams to understand infrastructure needs
Requirements
Nice to have: SRE and Platform Engineering leadership
Data engineers are responsible for building reliable and scalable data infrastructure that enables organizations to derive meaningful insights, make data-driven decisions, and unlock the value of their data assets.
Locations (4)
Ciudad de México, CDMX, Mexico
Edomex, Estado de México, Mexico
Aguascalientes, Aguascalientes, Mexico
Monterrey, Nuevo León, Mexico
Responsibilities
Diseñar e implementar pipelines de calidad de datos (Data Quality) sobre Google Cloud Platform.
Desarrollar y mantener soluciones de integración de datos utilizando Cloud Data Fusion.
Diseñar, desarrollar y optimizar procesos ETL y ELT.
Engineer responsible for designing, developing, and industrializing generative AI solutions on Google Cloud Platform.
Responsibilities
Cadrer et architecturer des solutions d'IA générative adaptées aux besoins métiers
Industrialiser des applications robustes en Python et intégrer les modèles d'IA via APIs, pipelines data et services cloud
Déployer et optimiser des solutions d'IA générative sur Google Cloud en garantissant performance, scalabilité, sécurité, observabilité et maîtrise des coûts
Accompagner et conseiller les équipes métiers et IT dans l'adoption des usages de l'IA générative
Requirements
Nice to have: Google Professional Cloud Architect
Nice to have: Professional Machine Learning Engineer
Intervention sur tout le cycle de vie du projet, du conseil au code pour concevoir, développer et industrialiser des solutions d'IA générative sur Google Cloud Platform.
Responsibilities
Cadrer et architecturer : analyser les besoins métiers pour concevoir des solutions d'IA générative adaptées (RAG, agents IA, optimisation de prompts et spécialisation de modèles)
Industrialiser : développer des applications robustes en Python et intégrer les modèles d'IA aux systèmes existants via APIs, pipelines data et services cloud
Déployer et optimiser : mettre en production des solutions d'IA générative sur Google Cloud en garantissant performance, scalabilité, sécurité, observabilité et maîtrise des coûts
Accompagner et conseiller : vulgariser les enjeux techniques auprès des parties prenantes et accompagner les équipes métiers et IT dans l'adoption de l'IA générative
Requirements
Nice to have: Certification Google Professional Cloud Architect
Nice to have: Professional Machine Learning Engineer
Embedded builder bridging frontier data solutions and production-grade reality for customers on Google Cloud’s Data Cloud team.
Responsibilities
Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL, and Python on BigQuery, Managed Spark, Spanner, and AlloyDB.
Stand up working proofs with customers’ data, including de-identifying sensitive data (e.g., PCI, PHI).
Build the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and the access controls that let custom agents act on trusted data.
Train, evaluate, and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.
Refactor and migrate high-cost analytics, Spark, and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.
Requirements
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience
Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL, and Python on BigQuery, Managed Spark, Spanner, and AlloyDB.
Responsibilities
Build and ship production data and machine learning workloads with customers, writing, testing, and performance-tuning PySpark, SQL, and Python on BigQuery, Managed Spark, Spanner, and AlloyDB.
Stand up working proofs with customers’ data, including de-identifying sensitive data (e.g., PCI, PHI).
Build the data foundation for agentic solutions, including lakehouse architectures, metadata and semantic context, and the access controls that let custom agents act on trusted data.
Train, evaluate, and deploy machine learning and agentic components where they improve the customer’s business metric, with evaluation and monitoring in place.
Refactor and migrate high-cost analytics, Spark, and operational database workloads from other platforms onto Google Cloud engines, with measured price-performance results.
Requirements
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
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