• Play a critical role in the design, implementation, and delivery of modern digital measurement and data solutions for our customers • Focus on implementing and managing digital analytics platforms, event tracking frameworks, customer data platforms, and data pipelines • Work closely with marketing teams, engineers, and data stakeholders to ensure reliable, scalable, and accurate data collection across web and mobile platforms • Translate business and marketing requirements into scalable tracking and measurement architectures • Communicate technical measurement concepts clearly to both technical teams and marketing stakeholders

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 Manager, Data Enablement leader owns the US DTC regional analytics data layer and reporting platform, being the bridge between the global data engineering organization (ARC) and US regional analytics teams. You will ensure global data products are usable, trusted, and scalable for regional reporting and analysis, while enabling a robust self-service analytics environment. You will combine data product integration, analytics engineering, BI platform governance, and technical enablement to support high-quality analytics and decision-making. About the Job Analytics Semantic Layer Ownership - Build the regional analytics layer that enables consistent reporting and scalable self-service - Develop and maintain the semantic layer on top of global data products - Create standardized datasets/models that advanced analytics use cases, reporting consistency across dashboards, self-service by business users, and AI integration - Manage transitional data pipelines and datasets before they are productionized by global data engineering Data Product Integration & Engineering Partnership - Be the primary regional interface to the global data engineering organization (ARC) - Lead QA and UAT processes for global data products used by the regional analytics team - Translate analytics needs into technical requirements and specifications for global data engineering - Advocate for regional priorities and ensure issues are properly triaged and prioritized within global engineering roadmaps - Manage major bug tracking and issue resolution workflows related to data products - Ensure successful adoption of new data platforms and architecture changes BI Platform Governance & Reporting Oversight - Ensure consistency, performance, and reliability across the regional reporting ecosystem - Establish query performance and development standards - Ensure regional definition alignment and source consistency across reporting; collaborate with global alignment workstreams - Coordinate reporting infrastructure changes affecting large sets of dashboards - Investigate and debug data or reporting issues across the BI environment - Implement minor reporting updates and improvements when needed - Coordinate with platform teams such as BI infrastructure, IT, and data engineering Technical Enablement for the Analytics Team - Build the analytics tools, infrastructure, and processes that allow the analytics team to work efficiently - Maintain and govern the analytics team's technical workspace (e.g., BigQuery/GCP, GitHub environments) - Establish development processes, repository structures, and code review standards - Maintain development tables and experimental pipelines before engineering productionization - Develop lightweight tools and technical solutions to support workflows About You - Technical languages: advanced SQL + Python; LookML - 5-10 years experience which includes the following: - Development of scalable data models utilizing SQL/Python; experience with data automation and transformation utilizing Python - Designing semantic layers, dimensional models, and datasets - Working with modern cloud data solutions and architecture (BigQuery preferred) including understanding of data architecture - Owning analytics orchestration tools (e.g. GitHub) - Developing within BI tools (Tableau & Looker Core) - Owning data governance and quality, including creating data quality frameworks, testing/UAT/QA processes, and metric alignment - Creating and managing self-service analytics environments, including reusable datasets for business users - AI experience (especially within BI) The expected starting salary range for this role is $112,000 - $164,200 per year. We may ultimately pay more or less than the posted range based on the location of the role. The amount a particular employee will earn within the salary range will be based on factors such as relevant education, qualifications, performance and business needs. Levi Strauss & Co. (LS&Co.) offers a total rewards package that includes base pay, incentive plans, 401(k) matching, paid leave, health insurance, product discounts, and more designed to help you and your family stay healthy, meet your financial goals, and balance the demands of your work and personal life. Available benefits and incentive compensation vary depending upon the specifics of the role; details relating to a specific role will be made available upon request. Read more about our benefits here. LS&Co. is an affirmative action and equal employment opportunity employer. We welcome and value people from diverse cultures, backgrounds, and experiences to make LS&Co. a collective success. #LI-remote EOE M/F/Disability/Vets LOCATION US-WAM-CORP FULL TIME/PART TIME Full time FILL DATE This position is expected to be filled by 06/16/2026.Current LS&Co Employees, apply via your Workday account.

Role Description Join Accenture Federal Services to drive positive, lasting change that moves missions and the government forward! - Architect and design databases, applications, and backend services - Develop and secure internal APIs, ETL processes, and integrations with external APIs - Lead DevOps processes, including build and release pipelines - Ensure robust data security across authentication, authorization, and access control layers - Apply data science expertise to guide model selection, implementation tradeoffs, and AI integration - Produce clear and comprehensive technical documentation - Communicate architectural decisions and rationale to client stakeholders and IT leadership - Deliver end‑to‑end solutions from design through deployment Qualifications - Experience with Dataverse and/or Power Platform - Experience with API/ETL development using Power Automate, Python, and/or Azure Data Factory - Security experience with roles, API authentication, and/or Entra ID - Experience with Analytics and AI tools (AI Builder, Azure OpenAI, and/or Azure AI Search) - CI/CD expertise in Azure DevOps - Understanding of data science fundamentals - Secret clearance required - US Citizenship Requirements - Preferred experience in delivering full solutions within consulting or client‑facing environments - Familiarity with broader Power Platform solution design and lifecycle management Benefits - Wide variety of benefits offered Compensation The pay range for the states of California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Washington, Vermont, the District of Columbia, and the city of Cleveland is: $100,200 — $203,400 USD

• Design and maintain datasets and table structures • Manage warehouse performance, partitioning, clustering, and cost optimization • Maintain access controls and permissions • Structure warehouse schemas to support revenue analytics and reporting • Build and maintain ETL / ELT pipelines from revenue systems into the warehouse • Integrate data from systems such as HubSpot, Salesforce, marketing and sales analytics platforms, sales engagement platforms, billing systems, and product analytics tools • Monitor pipeline health and resolve failures • Manage schema changes from upstream systems • Ensure reliable and timely data synchronization • Manage GitHub repositories • Design and maintain analytics-ready data models • Build models for accounts, contacts, opportunities, and pipeline data • Maintain tables and models used by BI tools such as Looker • Optimize queries and support derived tables used in reporting • Ensure consistent metric definitions across reporting layers • Dashboard creation for data validation • Implement data validation and testing • Monitor pipeline health and data freshness • Identify and resolve data inconsistencies • Maintain documentation for warehouse models and data definitions