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Why should you join dLocal?

dLocal enables the biggest companies in the world to collect payments in 40 countries in emerging markets. Integral brands rely on us to increase conversion rates and simplify payment expansion effortlessly. As both a payments processor and a merchant of record where we operate, we make it possible for our merchants to make inroads into the world’s fastest-growing, emerging markets.

By joining us you will be a part of an amazing global team that makes it all happen. Being a part of dLocal means working with 1000+ teammates from 30+ different nationalities and developing an international career that impacts millions of people’s daily lives. We are builders, we never run from a challenge, we are customer-centric, and if this sounds like you, we know you will thrive in our team.

**What’s the opportunity?**

As a Senior DataOps Engineer, you'll be a strategic professional shaping the foundation of our data platform. You’ll design and evolve scalable infrastructure on Kubernetes, operate Databricks as our primary data platform, enable data governance and reliability at scale, and ensure our data assets are clean, observable, and accessible.

**What will I be doing?**:

- Architect and evolve **scalable infrastructure** to ingest, process, and serve large volumes of data efficiently, using **Kubernetes** and **Databricks** as core building blocks.
- Design, build, and maintain **Kubernetes-based infrastructure**, owning deployment, scaling, and reliability of data workloads running on our clusters.
- Operate **Databricks as our primary data platform**, including workspace and cluster configuration, job orchestration, and integration with the broader data ecosystem.
- Work in improvements to existing **frameworks and pipelines** to ensure performance, reliability,



and cost-efficiency across batch and streaming workloads.
- Implement **release strategies** (e.g., blue/green, canary, feature flags) where relevant for data services and platform changes.
- Establish and maintain **robust data governance practices** (e.g., contracts, catalogs, access controls, quality checks) that empower cross-functional teams to access and trust data.
- Build a framework to move raw datasets into **clean, reliable, and well-modeled assets** for analytics, modeling, and reporting, in partnership with Data Engineering and BI.
- Define and track **SLIs/SLOs** for critical data services (freshness, latency, availability, data quality signals).
- Implement and own **monitoring, logging, tracing, and alerting** for data workloads and platform components, improving observability over time.
- Lead and participate in **on-call rotation** for data platforms, manage incidents, and run structured **postmortems** to drive continuous improvement.
- Investigate and resolve **complex data and platform issues**, ensuring data accuracy, system resilience, and clear root-cause analysis.
- Maintain high standards for **code quality, testing, and documentation**, with a strong focus on **reproducibility and observability**.
- Work closely with the **Data Enablement team, BI, and ML stakeholders** to continuously evolve the data platform based on their needs and feedback.




- Stay current with **industry trends and emerging technologies** in DataOps, DevOps, and data platforms to continuously raise the bar on our engineering practices.

**What skills do I need?**:

- Bachelor’s degree in **Computer Engineering, Data Engineering, Computer Science**, or a related technical field (or equivalent practical experience).
- Proven experience in **data engineering, platform engineering, or backend software development**, ideally in **cloud-native environments**.
- Deep expertise in **Python or/and SQL,** with strong skills building data or platform tooling.
- Strong experience with **distributed data processing frameworks** such as **Apache Spark** (Databricks experience strongly preferred).
- Solid understanding of **cloud platforms**, especially **AWS** and/or **GCP**.
- Hands-on experience with **containerization and orchestration**: Docker, Kubernetes / EKS / GKE / AKS (or equivalent)
- Proficiency with **Infrastructure-as-Code** (e.g., Terraform, Pulumi, CloudFormation) for managing data and platform components.
- Experience implementing **CI/CD pipelines** (e.g., GitHub Actions, GitLab CI, Jenkins, CircleCI, ArgoCD, Flux) for data workloads and services.
- Experience in **monitoring & observability** (metrics, logging, tracing) using tools like Prometheus, Grafana, Datadog, CloudWatch, or similar.
- Experience with **incident management**: Participating in or leading on-call rotations.
- Handling incidents and running postmortems
- Building automation and guardrails to prevent regressions
- Able to work **autonomously and collaboratively.**

Nice to have
- Experience designing and maintaining **DAGs with Apache Airflow** or similar orchestration tools (Dagster, Prefect, Argo Workflows).
- Familiarity with modern data formats and ta

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q1pgc

📌 Senior Dataops Engineer (Buenos Aires)
🏢 Dlocal
📍 Buenos Aires