dLocal: Senior DataOps Engineer

Headquarters: Barcelona / Madrid
URL: http://dlocal.com
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.
- Build and maintain CI/CD pipelines for data applications (DAGs, jobs, libraries, containers), automating testing, deployment, and rollback.
- 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
- Strong analytical thinking and problem-solving skills, comfortable debugging across infrastructure, network, and application layers.
- Able to work autonomously and collaboratively.
- Experience designing and maintaining DAGs with Apache Airflow or similar orchestration tools (Dagster, Prefect, Argo Workflows).
- Familiarity with modern data formats and table formats (e.g., Parquet, Delta Lake, Iceberg).
- Experience acting as a Databricks admin/developer, managing workspaces, clusters, compute policies, and jobs for multiple teams.
- Exposure to data quality, data contracts, or data observability tools and practices.
To apply: https://weworkremotely.com/remote-jobs/dlocal-senior-dataops-engineer