Data Analytics Practice Engineer for Customer Solutions

Google Inc.; Ciudad de México; Presencial; MXN 1.800.000 - 2.400.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

None

Descripción de la vacante

Google Cloud is seeking a Practice Customer Engineer (Data Analytics) to partner with Technical Sales, accelerate technical wins, and develop architectures, proofs‑of‑concepts, and demos for customers.

You will present to technical stakeholders and executives, translate requirements into practical solutions, and collaborate with Product/Engineering to advance AI‑driven analytics and modern Lake House architectures.

Formación

• Bachelor’s degree in a technical field or equivalent practical experience.
• 10 years of experience in a customer-facing technical role or equivalent engineering experience.
• Experience with data analytics, Big Data concepts, ETL/ELT pipelines, SQL, and database performance.
• Proficiency in Python, Java, or Go for prototyping and demonstrations.
• Experience presenting architectures to technical stakeholders and leadership.

Responsabilidades

• Drive technical wins for complex data analytics workloads and ensure rapid customer adoption.
• Deliver tailored end-to-end solutions across storage, transformation, and visualization.
• Design architectures incorporating AI-driven data agents and modern analytics tech.
• Lead migrations to Google Cloud and run successful proofs-of-concepts.
• Collaborate with GTM, Product, and Engineering to resolve customer feature requests.

Conocimientos

Data analytics

Big Data concepts

Presentation skills

Customer-facing

Programming languages (Python, Java,Go

Educación

Bachelor's degree in a technical field

Master's degree in CS/Engineering/Math

Herramientas

SQL

Spark/Dataproc

MLOps

Descripción del empleo

Google Cloud is seeking a Practice Customer Engineer (Data Analytics) to partner with Technical Sales, accelerate technical wins, and develop architectures, proofs‑of‑concepts, and demos for customers.

You will present to technical stakeholders and executives, translate requirements into practical solutions, and collaborate with Product/Engineering to advance AI‑driven analytics and modern Lake House architectures.