APPLYDJINN
Vacancy catalog
EPAM
Open role

Senior Data DevOps Engineer with Azure

EPAMTurkey
Work model
Hybrid
Experience
3+ years
Employment
Not specified
Compensation
Not disclosed
Technology signal
8 tags

Technology context

8

Parsed from the vacancy text; ordered by relevance to this role.

Machine LearningAzureDevOpsScalabilityDatabricksCI/CDAutomationData DevOps

Full listing

Role description

We are looking for a Senior Data DevOps Engineer with Azure to deploy, configure, and support a production-ready Databricks Data Platform. You will work closely with Data Engineering, ML, Platform, and QA teams to build and evolve CI/CD pipelines, troubleshoot deployment and workflow issues, maintain configuration management practices, and produce clear technical documentation across the platform.

Responsibilities

  • Deploy and configure the Data Platform in Databricks based on the approved architecture and solution designs, ensuring environments are production-ready, secure, and scalable
  • Work closely with cross-functional teams (Data Engineering, ML, Platform, QA) to design, implement, and evolve CI/CD pipelines and supporting tooling for data workflows and services
  • Diagnose and resolve issues in build/deploy pipelines, data workflows, and production workloads; participate in root cause analysis and implement preventive fixes
  • Develop, standardize, and maintain configuration management practices (infrastructure configuration, environment parameters, secrets, cluster policies) to ensure consistency across environments
  • Produce and maintain clear technical documentation covering deployment guides, operational runbooks, pipeline logic, and platform configuration

Requirements

  • 3+ years of experience in a Build Engineer, DevOps Engineer, Platform Engineer, or similar role supporting delivery and operations
  • Strong hands-on experience with Databricks, Azure Data Factory, Azure DevOps, and Microsoft Azure in general - including DataOps practices such as automated data pipeline deployment, environment promotion, and governance
  • Experience with MLOps (model deployment, monitoring, lifecycle automation for ML workloads) is considered an advantage
  • Strong communication and collaboration skills, with the ability to work effectively across engineering, data, and operations teams
  • English at B2 level or higher, able to participate in technical discussions and produce documentation in English