资深全栈工程师 - 数据密集型网络应用 - 个性化

About us
AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.
About AB InBev Growth Group
Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.
In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.
We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.
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.

Make systems observable and reliable

• Instrument services with metrics, structured logs, and distributed tracing from day one.

• Define SLIs and SLOs, alerts, and dashboards for the components you own, and take part in on-call.

• Diagnose and fix reliability issues, and lead incident response and blameless postmortems.

• Establish safe rollout practices: feature flags, canary releases, and fast rollback.

Design and raise the bar

• Write design docs for the components you deliver, compare alternatives with data and prototypes, and escalate risk to the Staff Architect.

• Raise the team's technical bar through code review, pairing, and standards for testing, observability, and safe rollout.

• Use coding agent harnesses such as Cursor, Claude Code, and Codex as part of your workflow, and review generated code against tests, types, and contracts.

What you'll need:

• Bachelor's degree in computer science, engineering, mathematics, or another quantitative field. A master's degree or PhD is a plus.

• Relevant experience designing, building, and operating distributed backend services in production, across coding standards, code review, build, automated testing, deployment, and operations.

• A track record of building features end-to-end and owning them in production.

• Relevant experience in software design for reliability and scale, on both new and existing systems.

• Deep command of at least one high-performance language, such as Java/Kotlin, Go, Rust, C++, or C#, including its memory model, concurrency model, and runtime.

• Ability to work productively in Python, the language of the current services.

• Hands-on experience with observability in production: metrics, tracing, logging, SLOs, and tools such as OpenTelemetry, Datadog, Grafana, or Prometheus.

• Proven experience with performance work: profiling, query optimization, caching strategies, and load testing.

• Production depth in distributed systems: timeouts, retries, idempotency, partial failure, streaming (SSE, WebSockets), and decisions based on tail latency (p95, p99).

• Relevant 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, with the ability to discuss architecture and trade-offs in depth.

• Advanced communication in English; Spanish is a plus.

Nice to have:

• Experience with serving layers for analytical data (OLAP, columnar stores, semantic layers) or with 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.

More about you:

• You are a builder: you like taking a feature from a blank page to production and owning it from then on.

• You spend most of your time writing, reviewing, and operating code.

• You treat observability and performance as part of the feature, not something added afterward.

• You influence architecture with evidence: measurements, prototypes, and explicit trade-offs.

• You go from the API to the interface when the feature needs it.

• You use tests, types, and contracts as the guardrails for your code and for generated code.

• You work well on a staff-level team where everyone operates what they ship.

What We Offer:

• Performance based bonus*

• Attendance Bonus*

• Private pension plan

• Meal Allowance

• Casual office and dress code

• Days off*

• Health, dental, and life insurance

• Medicines discounts

• WellHub partnership

• Childcare subsidies

• Discounts on Ambev products*

• Clube Ben partnership

• Scholarship*

• School materials assurance

• Language and training platforms

• Transport allowance

*Rules applied
Equal Opportunity & Affirmative Action:
AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.
The following fields are optional, but anticipate the information for your registration*.
Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company.
For more information:
Originally posted on Himalayas