{"id":1383189,"url":"https://alion.io/job/levi-strauss-staff-data-engineer","title":"Staff Data Engineer","company":{"id":1780263,"name":"Levi Strauss & Co.","domain":"levistrauss.com","url":"https://alion.io/company/levistrauss","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":82,"ghost_share":0,"stale_share":0.976,"repost_share":0.012,"time_to_fill_p50_days":20,"computed_at":"2026-10-08T05:49:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Mexico City, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":50000,"max_usd":121000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":564},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Java","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-09-02T00:00:00Z","employer_posted_date":"2026-09-02","last_verified_at":"2026-10-09T01:49:12Z","board_verified":true,"closed_at":null,"days_open":37,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":37},"description":"Job Location:Mexico City, Mexico\nCalling all originals: At Levi Strauss & Co., you can be yourself - and be part of something bigger.We’rea company of people who like to forge our own path and leave the world better than we found it. Whobelievethat what makes us different makes usstronger.Soadd your voice. Make an impact. Find your fit - and your future.\nThe 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.\nThis 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.\nAbout the Job\nTechnical LeadershipandArchitecture\nArchitect, design, and implement enterprise-scale data platforms, data pipelines, semantic layers, and data products that support analytical, operational, and AI use cases.\n\nLead the design of scalable, highly available, and cost-optimized cloud-native data solutions capable of processing large volumes of structured and unstructured data.\n\nEstablish engineering standards, design patterns, and best practices for data ingestion, transformation, modeling, governance, observability, and reliability.\n\nDrive architectural decisions and provide technical leadership for critical initiatives with long-term enterprise impact.\n\nData Product Development\nLead end-to-end development of data products from source system integration, ingestion, transformation, modeling, and delivery through consumption layers.\n\nDesign robust data contracts with upstream and downstream systems to improve reliability and trust in data assets.\n\nBuild and optimize high-performance batch, streaming, and near real-time data pipelines.\n\nDevelop semantic and context-aware data models that improve accessibility and usability of enterprise data.\n\nQuality, Reliability, andGovernance\nEstablish and implement enterprise data quality frameworks, monitoring, observability, alerting, and governance practices.\n\nDrive implementation of security controls, privacy requirements, encryption standards, and regulatory compliance requirements.\n\nDefine and enforce data standards that improve consistency, lineage, discoverability, and trust across data products.\n\nStrategic CollaborationandBusiness Partnership\nPartner with Product Managers, Architects, Data Scientists, Analysts, and business stakeholders to define technical roadmaps and delivery priorities.\n\nTranslate complex business problems into scalable technical solutions that create measurable business value.\n\nLead cross-functional initiatives spanning multiple engineering teams, business domains, and geographic regions.\n\nTechnical MentorshipandOrganizational Influence\nMentor and coach engineers through code reviews, architecture reviews, and technical guidance.\n\nInfluence engineering culture by evangelizing best practices, modern technologies, and continuous improvement initiatives.\n\nEvaluate emerging technologies and determine their applicability to simplify architecture, improve performance, and enhance platform capabilities.\n\nRepresent the Data Engineering organization in architecture reviews and leadership discussions.\n\nAbout You\nRequired Education\nBachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Mathematics, or related technical discipline.\n\nPreferred Education\nMaster's degree in Computer Science, Data Engineering, Artificial Intelligence, Data Science, or a related quantitative field.\n\nPreferred Certifications\nGoogle Professional Data Engineer\n\nGoogle Professional Cloud Architect\n\nAdditional Qualifications\nDemonstrated technical leadership in enterprise-scale data engineering environments.\n\nProven ability to influence architecture, engineering standards, and technology strategy across multiple teams.\n\nStrong communication skills with the ability to convey complex technical concepts to executive and non-technical audiences\n\nWork Experience\n10+ years of progressive experience in Data Engineering, Software Engineering, Data Platform Engineering, or Big Data development.\n\nProven experience designing, building, and operating large-scale data platforms, modern data warehouses, and cloud-native data ecosystems.\n\nDemonstrated success leading highly complex engineering initiatives from concept through production deployment and operationalization.\n\nExperience building and optimizing large-scale distributed processing systems supporting high-volume data ingestion, transformation, and analytics workloads.\n\nHistory of delivering enterprise data products supporting analytics, machine learning, customer intelligence, and operational decision-making.\n\nExperience influencing technical direction across multiple teams and mentoring engineers in architecture, engineering excellence, and delivery practices.\n\nExperience working in global, matrixed organizations and collaborating with cross-functional stakeholders across business and technology functions.\n\nSpecialized Knowledge,Technical Skills,Tools, andSystems\nData Engineering & Architecture\nAdvanced expertise in Data Modeling, Data Architecture, Data Warehousing, ETL/ELT, and modern data platform design.\n\nDeep understanding of distributed computing frameworks and large-scale data processing.\n\nProgramming & Development\nExpert-level proficiency in SQL.\n\nAdvanced proficiency in Python and/or Java.\n\nStrong software engineering fundamentals including design patterns, testing, code quality, and performance optimization.\n\nBig Data Technologies\nApache Spark\n\nFlink\n\nHive\n\nKafka / PubSub\n\nDistributed processing and streaming architectures\n\nCloud Platforms\nGoogle Cloud Platform (preferred)\n\nAWS\n\nMicrosoft Azure\n\nData Platforms & Analytics Technologies\nBigQuery\n\nDatabricks\n\nRedshift\n\nDBT\n\nPySpark\n\nModern semantic layer technologies\n\nData observability and monitoring platforms\n\nDevOps & Platform Engineering\nGitHub Enterprise\n\nCI/CD pipelines\n\nInfrastructure as Code (Terraform or equivalent)\n\nPlatform automation and deployment frameworks\n\nGovernance & Security\nData Governance\n\nData Privacy\n\nData Lineage\n\nAccess Controls\n\nRegulatory Compliance\n\nData Quality Frameworks\n\nObservability and Monitoring Solutions\n\nVisualization & Consumption\nLooker\n\nAnalytics and BI consumption platforms\n\nSemantic modeling and self-service analytics technologies\n\nLOCATION\nMexico, D.F., MexicoFULL TIME/PART TIME\nFull time Current LS&Co Employees, apply via your Workday account.","description_format":"text","description_chars":6805,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Mexico","iso":"MX","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Sportswear & Activewear"],"lifecycle":[{"event":"open","at":"2026-09-28T09:15:08Z"}],"visa":[],"liveness":{"score":20,"band":"cold","label":"Long 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