Senior DataOps Engineer

We are looking for a Senior DataOps Engineer in CABA to join our team and industrialize Databricks as corporate data platform, ensuring a standardized, automated, observable, and reliable model for developing and operating data products. The mission is to reduce manual tasks and individual dependencies by enabling reusable patterns and self-service capabilities, allowing the platform to scale without a proportional increase in the team’s operational burden.

Core skills you’ll bring

• 5+ years of experience in Data Platform Engineering, DataOps, DevOps, Site Reliability Engineering (SRE), or similar roles.

• Hands-on experience with Databricks in enterprise-scale environments.

• Strong experience implementing and managing CI/CD pipelines and Git-based development workflows.

• Proven experience with Infrastructure as Code, including Terraform (required) or equivalent technologies.

• Experience with cloud platforms, identity and access management (IAM), networking, secrets management, service principals, and infrastructure automation.

• Bachelor's degree in Engineering, Information Technology, Computer Science, Information Systems, or a related field.

• English - intermediate or higher

• Ability to visit the office once a week

The impact you'll create

• Design and implement CI/CD pipelines for Databricks, enabling controlled promotion of artifacts across environments.

• Develop and maintain Infrastructure as Code (IaC) solutions using Terraform and other automation tools.

• Automate provisioning and management of Databricks workspaces, permissions, compute resources, service principals, and platform policies.

• Create reusable templates, frameworks, and engineering standards for onboarding new products and data pipelines.

• Automate onboarding processes for users, applications, and services across the data platform.

• Implement and maintain observability capabilities, including monitoring, logging, alerting, and operational metrics.

• Define and manage operational reliability practices, including SLAs, SLOs, retry mechanisms, reprocessing workflows, and recovery procedures.

• Improve platform reliability, scalability, performance, and capacity management.

• Support FinOps initiatives through cost allocation, tagging strategies, usage tracking, and optimization activities.

• Document platform standards, architecture decisions, operational procedures, and runbooks.

• Drive a self-service culture by developing platform capabilities that reduce manual intervention and increase engineering productivity.