Jobs at AB InBev

Design, develop, and maintain scalable Java backend services using Spring Boot and microservices. Build RESTful APIs, optimize distributed applications, improve observability, and contribute to architecture decisions, testing, automation, CI/CD, and cloud-native deployments. Collaborate cross-functionally in Agile environments while supporting Docker- and Kubernetes-based applications.

Leads cross-functional technology programs from planning through delivery and post-launch stabilization. Translates business and technology objectives into execution plans, coordinates Product, Engineering, Data, Operations, and Business teams, and manages risks, dependencies, governance, timelines, and stakeholder communication. The role requires influencing without direct authority, navigating ambiguity, ensuring predictable delivery, and escalating critical issues appropriately while continuously improving program management practices.

Coordinate an engineering team delivering BEES products across Android, iOS, and Web platforms. Drive technical consistency, feature parity, deployment optimization, quality strategies, and scalable implementations. Collaborate with architects, project and product leaders to communicate status, resolve blockers, manage dependencies, and support rapid delivery in an Agile environment.

Develop and scale global integrated marketing campaign strategy, governance, calendars, performance frameworks, and best practices. Coordinate CRM, Retail Media, Product, Data, Ad Ops, agencies, and local markets. Establish audience recommendations, KPIs, campaign efficiency standards, dashboards, post-mortems, training materials, and HTML or gamification capabilities. Track investments and ROI while translating performance insights into actionable improvements across BEES markets.

Develop and maintain scalable Java backend services using Spring Boot and microservices architecture. Build RESTful APIs, improve observability, monitoring, and logging, and troubleshoot production issues. Collaborate in Agile Scrum teams, contribute to architecture decisions, code reviews, testing, automation, and CI/CD. Support technical problem-solving, maintain application quality, and help refine tasks and solutions.

Leads global communications intelligence by developing media, social, reputation, and public opinion measurement frameworks. Monitors earned media and cultural trends, analyzes sentiment and narratives, establishes reporting standards and benchmarks, and translates data into actionable recommendations for Communications and Marketing teams. The role also optimizes measurement tools, strengthens governance, and partners with global cross-functional teams and external agencies.

Owns global commercial performance reporting, volume forecasting, market share analysis, and performance management across AB InBev. Develops standardized reporting, analytical methodologies, and executive-ready insights to identify risks, opportunities, and business drivers. Partners with Commercial, Finance, Revenue Management, and regional teams to improve data quality and decision-making. Leads forecasting enhancements, performance decomposition, automation, visualization, and strategic analyses for senior leadership.

Lead partner discovery, fit-gap analysis, and implementation prototyping for the BEES B2B commerce platform. Translate partner operating models into platform capabilities, define feature scope, reject unnecessary customization, and align configured solutions with agreed requirements. Feed field insights into the product roadmap while collaborating with implementation engineering. Success is measured by fit-gap accuracy and feature completeness at go-live.

Owns technical configuration, data mapping, integrations, and production go-live readiness for partner implementations. Works with partner IT teams and MIS on connector scope, activates product features, creates runbooks and monitoring, and automates manual post-go-live processes. Success is measured by stable production operations, no SEV1 incidents after Hypercare, and reduced manual effort.

Provide end-to-end production support for live digital applications, troubleshooting application, data, integration, API, and mobile issues. Manage incidents and service requests, participate in war rooms and root-cause analysis, meet SLAs, maintain runbooks and knowledge bases, analyze trends, and improve reliability. Collaborate with engineering, QA, product, and project teams while mentoring junior analysts and presenting operational metrics.

Develop and maintain Java backend services, APIs, and microservices. Build integrations, RESTful and event-driven solutions, and implement scalable features. Collaborate with product, frontend, QA, and other engineering teams; write tested, maintainable code; participate in reviews; troubleshoot issues; and support observability, reliability, architecture, and continuous improvement initiatives.

Own partner implementations from kickoff through go-live and hypercare exit. Manage timelines, milestones, dependencies, commitments, escalations, technical feasibility alignment, and the handoff from implementation to support. Coordinate cross-functional pillar owners, protect feasible go-live dates, maintain the implementation playbook, and drive on-time delivery. Requires multi-workstream implementation experience, strong delivery-method knowledge, partner coordination skills, and advanced English and Spanish.

Build and maintain data platform tooling, shared libraries, data pipelines, automation workflows, and reusable lakehouse components. Develop observability integrations, CI/CD foundations, testing frameworks, schema and contract checks, and platform features that improve consistency and developer experience. Partner with data teams to support adoption through documentation, examples, and hands-on enablement. Work primarily with Python on Databricks and Azure Cloud infrastructure.

Leads a data engineering team responsible for building scalable safety solutions and data platforms. Owns technology roadmaps, architecture decisions, data pipeline design, cloud systems, data quality, monitoring, CI/CD optimization, and enterprise ETL/ELT. Recruits, coaches, and develops engineers while establishing engineering standards, testing practices, reusable frameworks, and reliable batch and streaming architectures supporting analytics and machine learning.

Serve as the primary technical advisor for partners after go-live, supporting platform adoption, troubleshooting issues, monitoring performance, and coordinating product updates. Manage implementation projects end-to-end, maintaining timelines, priorities, and roadmaps while aligning internal teams, partners, and executive stakeholders. Provide technical guidance to account managers and ensure solutions are delivered according to requirements and agreed commercial deadlines.

Analyze complex datasets and develop machine learning and deep learning models for optimization, recommendations, personalization, promotions, segmentation, imputation, and insights. Process large-scale data using Azure, Databricks, Spark, Python, and PySpark. Collaborate with data engineers, product managers, and business stakeholders in an agile environment while implementing scalable solutions and automation workflows.

Develop and maintain backend services, APIs, microservices, and event-driven solutions using Java and Kotlin. Implement features, integrations, and scalable architectures while collaborating with product, frontend, and QA teams. Write tested, maintainable code; participate in reviews; troubleshoot issues; and improve observability, reliability, development processes, and operational excellence. The role requires experience with Spring Boot, REST APIs, relational databases, messaging platforms, cloud services, Git, and software testing.

Leads product growth and digital insights for a business application, owning local product strategy, roadmap prioritization, discovery, requirements, and stakeholder alignment. Manages an AI and machine learning personalization squad developing recommendation, cross-sell, and upsell models. Owns data management, analytics, experimentation, impact measurement, and business insights to support data-driven decisions. The role bridges business, technology, and data teams across regional environments.

Design, develop, and maintain scalable Java backend services using Spring Boot and microservices architecture. Build RESTful APIs, integrations, and distributed applications while improving observability, performance, resilience, testing, and automation. Collaborate cross-functionally on architecture decisions and business solutions, contribute to CI/CD and cloud-native deployments, and support Docker- and Kubernetes-based applications.

Enhance user experiences for AI-driven data products within the BEES apps ecosystem. Analyze feedback and model performance, collaborate with customer support, data scientists, and product managers, manage user feedback loops, create training and documentation, monitor quality metrics, advocate for customers, and align global stakeholders on priorities and results.