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Work setup in the employer’s posting: TELECOMMUTEFrom the employer’s posting
Deploy and configure the Databricks data platform per approved architecture
Design, implement, and evolve CI/CD pipelines for data workflows and services
Diagnose and fix build, deploy, and production data workflow issues
Maintain configuration management for infrastructure, environments, secrets, and cluster policies
Write and maintain deployment guides, runbooks, and platform documentation
Deploy and configure the Data Platform in Databricks based on the approved architecture and solution designs
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
Develop, standardize, and maintain configuration management practices
Produce and maintain clear technical documentation covering deployment guides, operational runbooks, pipeline logic, and platform configuration
Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 2
Early. Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
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
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Yes. We’ve followed 10 other Senior Data DevOps Engineer with Azure jobs there since November 2026. All 10 are still up.
Large employers often post one title for several teams, so these can be separate openings.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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