In this role, you'll design, build, and optimize scalable, secure, and reusable data products on Google Cloud Platform. You'll bring together deep hands-on data engineering skills and practical AI engineering experience to make governed enterprise data available for analytics, reporting, and natural-language insight experiences.

You'll partner with Data Architects, BI Developers, Data Scientists, Product teams, and business stakeholders to build solid data foundations, standardize metric definitions, and enable trusted data access. The role calls for strong technical ownership, a hands-on approach to delivery, and the ability to mentor engineers while raising engineering standards.

Key Responsibilities

- Design, build, and maintain scalable, reusable, and cost-efficient data pipelines and data products on GCP.
- Develop resilient ETL/ELT pipelines for batch, near-real-time, and streaming workloads within a Medallion architecture.
- Build and optimize enterprise data models, including dimensional models and Star/Snowflake schemas.
- Deliver solutions using BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Cloud Functions, Cloud Run, Workflows, and Terraform.
- Optimize BigQuery for performance, cost, reliability, and governance, including partitioning, clustering, query tuning, and row- and column-level security.
- Build reusable APIs, services, and engineering tools with Python, Docker, Kubernetes, Cloud Run, and CI/CD.
- Work with architects, BI developers, data scientists, and business stakeholders to turn business needs into scalable, maintainable solutions.
- Help develop and refine governed semantic layers and standardized business metrics in Looker and other reporting tools.
- Define and champion best practices for data quality, testing, observability, documentation, metadata, lineage,



deployment, and operations.

AI-Enabled Data Engineering
- Turn governed data into clear, natural-language answers so teams can explore insights safely and efficiently.
- Design, build, and refine Skills, AI Agents, and conversational workflows that convert business questions into accurate, easy-to-understand insights.
- Develop and put into production AI patterns such as RAG, Text-to-SQL, agentic workflows, and tool-based AI orchestration.
- Connect governed BigQuery datasets and the Looker semantic layer to AI solutions, keeping metric definitions and business logic consistent.
- Build tools, guardrails, monitoring, and evaluation frameworks that reduce hallucinations, improve answer quality, and prevent unauthorized data exposure.
- Work with Data Science and AI teams to bring AI and ML use cases into production on reliable, governed, and scalable data foundations.

Agile Delivery & Technical Leadership
- Break broad business epics into clear, ready-to-build technical stories, tasks, and acceptance criteria.
- Take an active part in Agile ceremonies, backlog refinement, sprint planning, design reviews, and iterative delivery.
- Lead technically through architecture reviews, engineering standards, code reviews, and delivery guidance.
- Mentor Data Engineers and partner teams, supporting their growth and consistent use of best practices.

Requirements
- 7+ years of experience in data engineering,



data architecture, analytics engineering, or related technical roles.
- 4+ years of hands-on GCP experience designing and delivering production-grade data solutions.
- Deep hands-on expertise in BigQuery, SQL, Python, Dataflow and/or Dataproc, Pub/Sub, Cloud Composer, Cloud Run and/or Cloud Functions, and Terraform and CI/CD tooling.
- Strong track record designing and optimizing enterprise data models, ETL/ELT pipelines, and data platforms for structured, semi-structured, and unstructured data.
- Proven experience with BigQuery performance tuning, partitioning, clustering, cost optimization, and secure access controls.
- Advanced Looker semantic modeling skills, including governed metrics, explores, reusable dimensions, and consistent business definitions.
- Demonstrated hands-on experience delivering AI-enabled data solutions such as RAG, AI agents, Text-to-SQL, LLM-powered analytics, or conversational data experiences.
- Experience building secure, scalable APIs or services with Python and containerization tools like Docker and Kubernetes.
- Experience in Agile environments, turning business needs into actionable technical requirements.
- Strong communication, collaboration, and problem-solving skills.

Nice to Have
- Highly valued: experience with Sell-Out and/or Supply Chain data, including data structures, metrics, reporting, and operational processes.
- Experience with Power BI, DAX, Tableau, Looker Studio, or other enterprise BI tools.
- Experience with AI evaluation, prompt engineering, LLM observability, model safety controls, or responsible AI governance.
- Google Cloud Professional Data Engineer certification.
- Exposure to ReactJS or building lightweight internal web apps

📌 Senior GCP Data Engineer (Ciudad de México)
🏢 LaPieza
📍 Ciudad de México