Staff DataOps Engineer

Nodi; São Paulo; Hybrid; BRL 180,000 - 300,000; Full time

Job summary

dLocal is seeking a DataOps Technical Referent to build and operate a scalable data mesh. You will automate infrastructure, improve platform operations, and help teams translate requirements into practical solutions.

You’ll work across cloud, data engineering, and analytics with a focus on Databricks platform administration and IaC. This role suits a hands-on leader thriving in fast-moving environments.

Qualifications

• Proficient in Python, Bash, and SQL for data engineering tasks.
• Hands-on experience with Terraform and infrastructure as code.
• Experience administering Databricks and managing workspace configurations.
• Experienced with cloud platforms, especially AWS; GCP knowledge is a plus.
• Familiar with data models, data warehouses, and data pipelines.
• Strong collaboration and ability to work with ambiguity in fast-moving environments.

Responsibilities

• Build and maintain Python components, Terraform modules, and CI/CD pipelines.
• Administer Databricks workspaces and enforce access controls and compute policies.
• Manage Databricks infrastructure as code across environments.
• Support IAM, secrets, networking, and integrations with cloud and security teams.
• Monitor reliability, performance, and cost of Databricks workloads.
• Develop data models and documentation; promote DataOps and DevOps practices.
• Collaborate with stakeholders to clarify requirements and drive solutions to resolution.

Skills

Python

Bash

SQL

Terraform

Infrastructure as code

Git-based workflows

CI/CD

Databricks

AWS

GCP

Docker

Kubernetes

Helm

Databases & data pipelines

Data modeling

GitHub

Monitoring & troubleshooting

Streaming (Kafka/Kinesis)

Event-driven architectures

Security & IAM

Collaboration

Adaptability

AWS certifications (plus)

Tools

Databricks

Terraform

GitHub

Docker

Kubernetes

Helm

AWS

GCP

Job description

Job Type Full-time

Seniority Level 4: Specialist / Technical Referent / Team Lead

Workplace Hybrid

Department Technology

About dLocal

dLocal started with one goal - to close the payments innovation gap between global enterprise companies, and customers in emerging economies. We have over 1000 payment methods, in more than 60 countries. We are relentlessly focusing on serving our customers and solidifying our position as the preferred infrastructure solution for Global Merchants across Emerging Markets. With the ability to accept local payment methods and facilitate cross-border fund settlement worldwide, our merchants reach billions of underserved consumers in the high-growth markets of Africa, Asia, and Latin America. dLocal offers the ideal payment solutions for global commerce:

Payins: Accept local payment methods

Payo uts: Compliantly send funds cross-bor der

dLocal for Platforms: Unify your platform's payment solution

Financial technology for markets of the future.

We're looking for a DataOps Technical Referent to help build and operate a reliable, secure, and scalable data-mesh platform. You'll combine hands-on engineering with technical leadership: automating infrastructure and workflows, improving platform operations, and helping teams turn requirements into practical solutions.

You'll work across cloud infrastructure, data engineering, and analytics, with a focus on Databricks platform administration and infrastructure as code. This role suits someone who thrives in fast-moving environments, is comfortable with ambiguity, and enjoys learning and collaborating across teams.

What will I be doing?

Build and maintain Python components, Terraform modules, and CI/CD pipelines to automate data platform processes and infrastructure.

Administer and support Databricks workspaces and platform capabilities, including workspace configuration, access controls, compute policies, and operational standards.

Manage Databricks infrastructure as code, applying reusable, version-controlled patterns for provisioning and configuration across environments.

Support Databricks identity and access management, permissions, secrets, networking, and integrations in partnership with cloud and security teams.

Help operate and optimize Databricks workloads, monitoring reliability, performance, and cost; investigate issues and coordinate their resolution.

Integrate APIs and platform tools to automate data operations, trigger and monitor processes, and connect infrastructure and services.

Deploy, maintain, and oversee cloud infrastructure to meet reliability and performance needs.

Help develop data models, platform best practices, and technical documentation for users.

Establish and improve DataOps and DevOps practices, policies, and processes.

Work with stakeholders to understand needs, clarify requirements, and propose solutions; take ownership of issues and drive them through to resolution.

Contribute to platform architecture and technology decisions, weighing trade-offs and promoting sound data access, stewardship, and governance practices.

What skills do I need?

Strong Python skills; familiarity with Bash and the ability to work confidently with SQL.

Practical experience with Terraform and infrastructure as code, Git-based workflows, and CI/CD.

Experience administering or operating Databricks, ideally including workspace configuration, access management, compute, and platform automation.

Familiarity with cloud environments and services, particularly AWS; experience with GCP is also valuable.

Understanding of databases, data warehouses, data lakes, data pipelines, and data modeling.

Familiarity with development and data tools such as GitHub, Docker, Kubernetes, and Helm.

Strong troubleshooting and process-improvement skills, with attention to operational reliability, security, and detail.

Ability to collaborate across teams, communicate technical options clearly, and own work with limited guidance.

Adaptability and initiative: you're comfortable with rapid iteration, ambiguity, and finding creative solutions.

AWS associate- or professional-level certifications are a plus.

Experience with streaming data concepts and technologies, such as Kafka or AWS Kinesis, including building or supporting reliable streaming pipelines.

Understanding of event-driven architectures, message delivery and processing patterns, and monitoring/troubleshooting of streaming workloads.

How you’ll work

You'll partner with data, engineering, and infrastructure stakeholders to make the platform easier to operate and safer to change. You'll balance hands-on delivery with reusable standards, clear documentation, and continuous improvement.