Staff Full-Stack Engineer - Data Intensive Web-Applications - Personalization
Staff-level, hands-on full-stack engineer (backend focus) at AB InBev's Growth Group in Campinas, building and operating the backend of a web analytics and AI portal: APIs, the read data layer over analytical data, auth, and integration with AI agent services. Uses Python, TypeScript, and a high-performance language, with heavy focus on performance, observability, and reliability.
- Compensation
- Not disclosed
- Location
- Campinas, São Paulo, Brazil
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- Not specified
- Category
- Fullstack
- Posted
Skills
Must have
- Bachelor's degree in computer science, engineering, mathematics, or another quantitative field
- Experience designing, building, and operating distributed backend services in production (coding standards, code review, build, automated testing, deployment, operations)
- Track record of building features end-to-end and owning them in production
- Experience in software design for reliability and scale, on both new and existing systems
- Deep command of at least one high-performance language (Java/Kotlin, Go, Rust, C++, or C#), including memory, concurrency, and runtime
- Ability to work productively in Python
- Hands-on production observability: metrics, tracing, logging, SLOs, and tools such as OpenTelemetry, Datadog, Grafana, or Prometheus
- Proven performance work: profiling, query optimization, caching strategies, load testing
- Production depth in distributed systems: timeouts, retries, idempotency, partial failure, streaming (SSE, WebSockets), tail latency (p95, p99)
- Experience delivering full stack to production with TypeScript and a modern web framework
- Day-to-day fluency with coding agent harnesses such as Cursor, Claude Code, and Codex, including critical review of generated code
- Clear written and verbal communication; advanced communication in English
Nice to have
- Master's degree or PhD
- Spanish communication skills
- Experience with serving layers for analytical data (OLAP, columnar stores, semantic layers) or data platform integration
- Experience integrating applications with LLM or agent services as a consumer
- Experience with B2B, multi-country, or multi-tenant products
- Experience with cloud (Azure, AWS, or GCP), containers, and Kubernetes
Full description
About BEES
Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely.
At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!
About the Role:
As a Staff Full Stack Engineer (Backend Focus), you will build and operate the backend of a Web Analytics and AI portal. That includes the APIs that serve the application, the read data layer over analytical data, authentication and authorization, and the integration points with the services the rest of the team builds. The portal gives commercial intelligence to the people who make business decisions, through daily briefings, customizable KPIs, analytical insights, geographic exploration, and conversational analytics.
This is a Staff (L6), hands-on individual contributor role on a lean team where everyone is Staff: a Staff Architect, Staff AI Engineers, Staff Software Engineers, and Data Engineers. Architectural direction sits with the Staff Architect, and the AI Engineers own the agents. You are the builder: you take features from the design doc to production, end to end, and you make them fast, observable, and reliable. Most of your time goes to writing, reviewing, and operating code, including the user interface when the delivery calls for it.
What you'll do:
Build the backend and ship features end to end
- Design, implement, test, and operate backend services against explicit latency, availability, and cost targets.
- Implement typed, versioned API contracts and evolve them without breaking clients.
- Own authentication, authorization, and multi-tenant access control across the portal.
- Ship features end to end, from the API to the interface, when the delivery calls for it.
- Integrate the application with internal services, including the agent services built by the AI Engineers. This covers response streaming, timeouts, cancellation, and partial failures.
Own the read path and performance
- Own the read path over analytical data: data modeling, query plans, pagination, batching, caching, and consistency trade-offs.
- Set and defend performance budgets. Profile services, reduce tail latency (p95, p99), and optimize cost per request.
- Run load and capacity tests before launches, and use the results to make design decisions.