Tech Lead Software Engineer - Reliability

Nubank; São Paulo; Presencial; BRL 180 000 - 320 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity

Meal Card

Public Transportation

Medical Plan

Dental Plan

Work-from-home Allowance

Relocation Assistance

Resumo da oferta

Nubank seeks an experienced Production Reliability Engineer in São Paulo to join the U.S. Market team. The role focuses on reliability, scale, and operational excellence, shaping production-ready systems for high-demand use.

You will define SLOs/SLIs, lead incident responses, and drive postmortems, while reducing toil and improving tooling across squads. Hybrid work model with strategic collaboration remains core.

Qualificações

• Experience defining and operating against SLOs/SLIs, and using error budgets to influence decisions.
• Experience leading incident response and writing postmortems that produced durable improvements.
• Hands-on experience with observability tooling (metrics, logs, tracing) to diagnose production issues.
• Deep system design experience: distributed services, messaging, storage tradeoffs, API design, idempotency, backpressure.
• Significant experience building and operating production software in high-ownership environments.

Responsabilidades

• Define and operate against SLOs/SLIs, using error budgets to influence decisions.
• Build the observability layer with metrics, logs, traces and alerts.
• Lead incident response; drive blameless postmortems and durable improvements.
• Reduce toil through automation and better tooling; avoid repetitive operational work.
• Hardening production: stress-test designs for failure, degradation, and spikes.
• Make changes safe and fast with progressive delivery, canaries, and migrations.
• Improve developer experience by removing operational friction and gaps.
• Collaborate with product, platform, security, and other teams across disciplines.
• Raise engineering bar through mentoring and architectural reviews.

Conhecimentos

SLOs/SLIs management

Incident response leadership

Observability tooling

System design & distributed services

Cloud platforms (AWS/GCP)

CI/CD pipelines

Mentoring engineers

Pragmatism under high velocity

Ferramentas

AWS

GCP

Kubernetes

CI/CD

Descrição da oferta de emprego

About Nu

Nu is the leading digital bank in Latin America, serving 135 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. Visit our Institutional Page

About the role

The U.S. Market team is launching a differentiated financial product in the largest and most demanding financial market in the world. We’re iterating quickly on real customer signals while building systems that will eventually serve customers at Nubank scale. That combination - early-stage velocity, regulatory weight, and high reliability expectations, requires an engineer whose primary mandate is reliability, scale, and operational excellence.

This role exists to make sure the systems we’re building today can be trusted in production tomorrow, and to set the bar for what "production-ready" means on this team. The engineer in this role delivers their mandate by writing production code, shaping architecture, and engineering the systems themselves - not by absorbing operational load.

You'll be responsible for

Define and operate against SLOs. Establish meaningful SLIs and SLOs with product and engineering partners, manage error budgets, and use them as real inputs to prioritization rather than dashboards no one reads.

• Build the observability layer. Improve metrics, logs, traces, and alerting so issues are detected early, attributed precisely, and debugged with code-level confidence. Push instrumentation upstream into the services we own.
• Lead incident response. Act as incident commander when needed, drive blameless postmortems, and turn findings into concrete engineering work that lands. Build the muscle in the team so this isn’t centralized in any one person.
• Reduce toil through engineering. Identify repetitive operational work and eliminate it with software - automation, self-healing behavior, better defaults, better tooling - rather than absorbing it as ongoing overhead.
• Production Hardening. Stress-test designs for partial failure, dependency degradation, traffic spikes, and adversarial inputs. Run capacity and performance work before incidents arise. Ensure resiliency primitives are tuned and working correctly.
• Make change safe and fast. Improve release safety through progressive delivery, feature flags, canaries, rollbacks, and tested migrations. Help the squad ship faster and with lower blast radius.
• Improve developer experience especially where it removes operational friction or improves change safety. Where internal tooling or platform gaps slow the team down, build or contribute the fix. Prefer leverage over heroics.
• Partner across disciplines. Work closely with product, platform, security, compliance, and other engineering teams. Translate reliability and risk tradeoffs into language each audience can act on.
• Raise the engineering bar. Mentor engineers, review hard designs and PRs, and shape technical standards across the squad. Lead through clarity and judgment, not authority.

We are looking for a person who has

Track record of owning services in production - not just shipping them, but being the engineer responsible for how they behave under real load and real failure.

• Experience defining and operating against SLOs/SLIs, and using error budgets to influence engineering and product decisions.
• Experience leading incident response and writing postmortems that produced durable improvements.
• Hands-on experience with observability tooling (metrics, structured logging, distributed tracing) and using it to diagnose nontrivial production issues.
• Deep system design experience: distributed services, asynchronous messaging, storage tradeoffs, API design, idempotency, consistency, backpressure, and graceful degradation.
• Significant industry experience building and operating production software systems in a high-ownership engineering environment.
• Comfort operating in modern cloud environments (e.g., AWS/GCP), containerized workloads, and CI/CD pipelines, and reasoning about their failure modes.
• Demonstrated technical leadership: influencing architecture across teams, mentoring strong engineers, and making the people around you better.
• Pragmatism. You can hold a high reliability bar while still helping a fast-moving squad ship.

Location for this opportunity (City, Country)

• São Paulo, Brazil

Our Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare - Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage - Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
• Relocation Assistance Package, if applicable

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

Explore how we build technology at Nubank:

building.nubank.com.br

youtube.com/@building.nubank

Spotify

Our recruitment process may involve the use of artificial intelligence-enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers.