Staff Data Engineer

Job Location:  Mexico City, Mexico   Calling all originals: At Levi Strauss & Co., you can be yourself — and be part of something bigger.   We’re   a company of people who like to forge our own path and leave the world better than we found it. Who   believe   that what makes us different makes us   stronger.   So   add your voice. Make an impact. Find your fit — and your future.   The Staff Data Engineer is the technical leader for complex data engineering initiatives, responsible for designing and delivering scalable, secure, and high-performing data platforms, pipelines, and data products that enable enterprise analytics, AI, and business decision-making. This role solves complex data challenges at scale, establishes engineering standards, influences data architecture and platform strategy, and translates business priorities into resilient technical solutions. It supports modern cloud-native data ecosystems for analytical, operational, and AI workloads while ensuring data quality, reliability, security, governance, and cost efficiency.

Technical Leadership   and  Architecture Architect, design, and implement enterprise-scale data platforms, data pipelines, semantic layers, and data products that support analytical, operational, and AI use cases. Lead the design of scalable, highly available, and cost-optimized cloud-native data 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. Drive architectural decisions and provide technical leadership for critical initiatives with long-term enterprise impact. Data Product Development Lead end-to-end development of data products from source system integration, ingestion, transformation, modeling, and delivery through consumption layers. Design robust data contracts with upstream and downstream systems to improve reliability and trust in data assets. Build and optimize high-performance batch, streaming, and near real-time data pipelines. Develop semantic and context-aware data models that improve accessibility and usability of enterprise data. Quality, Reliability , and  Governance Establish and implement enterprise data quality frameworks, monitoring, observability, alerting, and governance practices. Drive implementation of security controls, privacy requirements, encryption standards, and regulatory compliance requirements. Define and enforce data standards that improve consistency, lineage, discoverability, and trust across data products. Strategic Collaboration   and  Business Partnership Partner with Product Managers, Architects, Data Scientists, Analysts, and business stakeholders to define technical roadmaps and delivery priorities. Translate complex business problems into scalable technical solutions that create measurable business value. Lead cross-functional initiatives spanning multiple engineering teams, business domains, and geographic regions. Technical Mentorship   and  Organizational Influence Mentor and coach engineers through code reviews, architecture reviews, and technical guidance. Influence engineering culture by evangelizing best practices, modern technologies, and continuous improvement initiatives. Evaluate emerging technologies and determine their applicability to simplify architecture, improve performance, and enhance platform capabilities. Represent the Data Engineering organization in architecture reviews and leadership discussions.

Required Education Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Mathematics, or related technical discipline. Preferred Education Master's degree in Computer Science, Data Engineering, Artificial Intelligence, Data Science, or a related quantitative field. Preferred Certifications Google Professional Data Engineer