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.

About the Job 

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. 

About You 

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

• Google Professional Cloud Architect

Additional Qualifications

• Demonstrated technical leadership in enterprise-scale data engineering environments.

• Proven ability to influence architecture, engineering standards, and technology strategy across multiple teams.

• Strong communication skills with the ability to convey complex technical concepts to executive and non-technical audiences

Work Experience

• 10+ years of progressive experience in Data Engineering, Software Engineering, Data Platform Engineering, or Big Data development. 

• Proven experience designing, building, and operating large-scale data platforms, modern data warehouses, and cloud-native data ecosystems. 

• Demonstrated success leading highly complex engineering initiatives from concept through production deployment and operationalization. 

• Experience building and optimizing large-scale distributed processing systems supporting high-volume data ingestion, transformation, and analytics workloads. 

• History of delivering enterprise data products supporting analytics, machine learning, customer intelligence, and operational decision-making. 

• Experience influencing technical direction across multiple teams and mentoring engineers in architecture, engineering excellence, and delivery practices.

• Experience working in global, matrixed organizations and collaborating with cross-functional stakeholders across business and technology functions. 

Specialized Knowledge, Technical Skills, Tools, and Systems

Data Engineering & Architecture

• Advanced expertise in Data Modeling, Data Architecture, Data Warehousing, ETL/ELT, and modern data platform design.

• Deep understanding of distributed computing frameworks and large-scale data processing.

Programming & Development

• Expert-level proficiency in SQL.

• Advanced proficiency in Python and/or Java.

• Strong software engineering fundamentals including design patterns, testing, code quality, and performance optimization.

Big Data Technologies

• Apache Spark

• Flink

• Hive

• Kafka / PubSub

• Distributed processing and streaming architectures

Cloud Platforms

• Google Cloud Platform (preferred)

• AWS

• Microsoft Azure

Data Platforms & Analytics Technologies

• BigQuery

• Databricks

• Redshift

• DBT

• PySpark

• Modern semantic layer technologies

• Data observability and monitoring platforms

DevOps & Platform Engineering

• GitHub Enterprise

• CI/CD pipelines

• Infrastructure as Code (Terraform or equivalent)

• Platform automation and deployment frameworks

Governance & Security

• Data Governance

• Data Privacy

• Data Lineage

• Access Controls

• Regulatory Compliance

• Data Quality Frameworks

• Observability and Monitoring Solutions

Visualization & Consumption

• Looker

• Analytics and BI consumption platforms

• Semantic modeling and self-service analytics technologies