The infrastructure mindset transfers directly. A DevOps engineer who understands how to automate deployments, manage cloud resources, and monitor systems already thinks like a data engineer — they just need to apply that thinking to data workflows instead of application delivery.
Why companies increasingly want both
Modern data platforms are cloud-native. They run on Kubernetes clusters, deploy pipelines through CI/CD systems, use Terraform for infrastructure, and require engineers who understand containerization, networking, and IAM policies. A data engineer who also understands DevOps practices is significantly more useful than one who only knows the data layer.
This is increasingly recognised in job descriptions. Companies building serious data platforms — GCCs, fintech firms, data-heavy product companies — actively prefer candidates who bring both perspectives. The term "DataOps" exists precisely because the overlap has become large enough to warrant a name.
What to focus on first
Start with SQL. If you can already script in Bash or Python, Python for data work will feel familiar quickly. The larger gap for most DevOps engineers is data modelling — understanding how data should be structured inside a warehouse, how to write clean transformation logic, and how ETL pipelines are designed to be idempotent and testable. That thinking is different from infrastructure automation, but it is learnable.
Within three to four months of focused learning, most DevOps engineers can produce working data pipelines. The cloud knowledge they bring makes deployment and infrastructure components feel natural from day one.