Senior Data Platform Architect & Analytics Lead

Levi Strauss & Co.; Ciudad de México; Presencial; MXN 1.200.000 - 1.900.000; Jornada completa

Descripción de la vacante

Levi Strauss & Co. is seeking a Staff Data Engineer in Mexico City to lead complex data platforms, pipelines, and data products enabling analytics, AI, and business decisions.

You will drive data architecture, governance, and scalable cloud-native solutions while ensuring security, reliability, and cost efficiency across enterprise workloads. The role requires strong leadership, collaboration with product managers, data scientists, and engineers.

Formación

• Bachelor's degree in Computer Science or a related field.
• 10+ years of progressive experience in Data Engineering or related roles.
• Experience building enterprise-scale data platforms and cloud-native ecosystems.
• Strong SQL and Python/Java proficiency.

Responsabilidades

• Lead the design of enterprise-scale data platforms, pipelines, and data products.
• Lead end-to-end data product development from source to consumption layers.
• Define data contracts with upstream/downstream systems for reliability.
• Establish data quality, governance, and security practices.
• Mentor engineers and influence architectural standards across teams.

Conocimientos

SQL

Python

Java

Data Modeling

Data Warehousing

ETL/ELT

Cloud Platforms

Big Data

Data Governance

Educación

Bachelor's degree in CS/related field

Master's degree preferred

Google Professional Data Engineer

Google Professional Cloud Architect

Herramientas

Google Cloud Platform

AWS

Azure

Databricks

Kafka

dbt

BigQuery

Looker

Descripción del empleo

Levi Strauss & Co. is seeking a Staff Data Engineer in Mexico City to lead complex data platforms, pipelines, and data products enabling analytics, AI, and business decisions.

You will drive data architecture, governance, and scalable cloud-native solutions while ensuring security, reliability, and cost efficiency across enterprise workloads. The role requires strong leadership, collaboration with product managers, data scientists, and engineers.