Staff Data Engineer

About the role

Levi Strauss & Co. is seeking a Staff Data Engineer to lead complex data engineering initiatives in Mexico City. The role will design and deliver scalable, secure, and high‑performing data platforms, pipelines, and products that power analytics, AI, and business decision‑making across the enterprise.

Key responsibilities

• Architect and implement enterprise‑scale, cloud‑native data platforms, pipelines, semantic layers and data products for analytical, operational and AI use cases.
• Design scalable, highly available, cost‑optimized solutions capable of processing large volumes of structured and unstructured data.
• Establish engineering standards, design patterns and best practices for data ingestion, transformation, modeling, governance, observability and reliability.
• Lead end‑to‑end development of data products, including source integration, ingestion, transformation, modeling and delivery to consumption layers.
• Build and optimise batch, streaming and near‑real‑time pipelines and define robust data contracts with upstream and downstream systems.
• Implement data quality frameworks, monitoring, alerting and governance practices, and enforce security, privacy and regulatory compliance.
• Partner with product managers, architects, data scientists, analysts and business stakeholders to translate business priorities into technical solutions.
• Drive cross‑functional initiatives across multiple engineering teams to deliver long‑term enterprise impact.

Required profile

• Proven experience leading large‑scale data engineering projects in a cloud environment.
• Strong understanding of data architecture, platform strategy and modern data ecosystems.
• Demonstrated ability to define and enforce data standards, quality, security and governance.
• Excellent collaboration skills with product, analytics and business teams.

Required skills

• Data platform design and implementation
• Cloud‑native data solutions (e.g., AWS, GCP, Azure)
• Batch, streaming and real‑time data pipelines
• Data modeling and semantic layer development
• Data quality, observability and governance frameworks
• Security, privacy and compliance controls