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España
Hybrid
dLocal (NASDAQ: DLO) is the payments platform connecting global merchants with billions of emerging-market consumers. We run mission-critical infrastructure across LatAm, Africa, and Asia, at volumes and regulatory complexity that leave no margin for unreliable delivery. Our engineering organization ships to production many times a day and is running one of the most ambitious AI-assisted engineering transformations in fintech.
Most companies are learning the hard way that giving engineers AI tools and hoping for the best produces more code, not better software. Deployment frequency rises; change failure rate rises with it. Rollbacks become routine. Codebases drift out of architectural consistency as teams lean on different assistants with different defaults. The productivity win gets consumed by rework, incidents, and technical debt.
We are not running that experiment. We believe the platform is the distribution layer for AI inside an engineering organization, and that effective productivity — shipping faster without shipping more defects — is only achievable when standardization, reliability, and AI adoption are owned by one leader with the mandate and seniority to drive change across the org.
That's the opportunity. You will own how dLocal builds and ships software — the platform, the paved roads, the reliability posture, and the way AI is embedded into the engineering workflow — so that every engineer at dLocal is demonstrably more productive and demonstrably safer in production six months after you start. You will have direct exec-level visibility on dLocal's automation program, a seat at the engineering strategy table, and the mandate to reshape how hundreds of engineers work.You are accountable for two integrated outcomes: effective engineering productivity — velocity that survives contact with production, not velocity for its own sake — and production reliability — the SRE posture, rollback guarantees, and incident response that let teams move fast without breaking payments. These are not two jobs. They are one job, because the platform is the mechanism through which both are delivered.
Own the IDP. Standardized service templates, CI/CD pipelines, deployment tooling, feature-flag infrastructure, progressive delivery, automated rollback. Golden paths for each tech stack we currently operate, faster to use than to work around. A platform-as-a-product discipline where dLocal engineers are your customers.
Own SRE and production excellence. Reliability engineering across dLocal's payment platforms. Observability standards. Incident management maturity. Production readiness reviews. On-call health. Error budgets. The operational contract between platform and product engineering teams.
Own AI-assisted engineering, done right. You own how AI shows up in the developer workflow at dLocal. That means the evolution of dCoder — our proprietary AI code generation platform built on advanced models from Anthropic, OpenAI, and others — the integration of third-party assistants where they add value, the prompts and context engineering that keep AI output consistent with our architectural standards, and the guardrails that stop AI-generated code from reaching production without the same review, testing, and observability we require of any other change. You will ship tools, teach teams how to use them, measure the outcomes, and iterate.
Own standards and architectural consistency. You partner with our Principal engineering community and the Engineering Bar Raiser Program to define and enforce the standards that let dLocal scale without fragmenting. Code review, testing, deployment, and AI usage standards.
Drive organizational influence and adoption. Owning the platform is 30% of the job. The other 70% is changing how hundreds of engineers work. You will lead the adoption program: enablement, internal comms, champions, migration plans, deprecation discipline. You measure success by how many teams are on the paved road, not by how many roads you have paved.
Deliver against the metrics that actually matter. Not vanity numbers. Deployment frequency and lead time for changes reported alongside change failure rate, time to restore, and code turnover rate — the four traditional DORA metrics plus the AI-era additions. SLO attainment on critical payment paths, rollback success rate, paved-road adoption, AI-assisted PR defect density versus human-written baseline. A deployment frequency win that comes with a CFR regression is not a win.
You have run platform engineering, SRE, or a combined function at a meaningful scale — hundreds of engineers, production systems with real consequences when they fail. Fintech, payments, or other regulated high-stakes environments are a strong plus. You speak credibly to both a Principal engineer debating rollback semantics and an exec member asking why automation rate moves the P&L.
You have strong opinions about the DORA framework and its limits in an AI-assisted world. You can articulate, without prompting, why change failure rate and code turnover matter more than deployment frequency as AI adoption scales. You have seen the "more code, more defects" failure mode and you have an opinion on how to prevent it.
You have hands-on familiarity with modern AI-assisted development tooling — GitHub Copilot, Cursor, Claude Code, agentic coding platforms — and ideally experience evaluating, deploying, or building in-house AI coding capabilities. You understand that a foundation model wrapper is not a product, and that the difference between a vibe-coding accelerator and a durable productivity system is context, standards, and measurement.
You are a builder of influence, not just of systems. You have taken a large engineering organization through a meaningful change in how they work — trunk-based development, a platform migration, a new reliability standard, an AI adoption program — and you can describe the adoption program in detail, because you designed and ran it.
You operate fluently in English.
What will make you stand out: experience with an in-house AI coding platform beyond simple IDE integration — prompt engineering, context systems, agentic patterns, evaluation frameworks for AI-generated code. A track record of driving the change failure rate down while deployment frequency goes up, with data to prove it. Experience in regulated financial services with production payment systems. A public point of view — writing, talks, open-source contributions — on platform engineering, reliability, or the responsible application of AI to software delivery.
