DataOps Engineer in the Making | DevOps → Data Engineering → DataOps
I didn't just switch roles — I crossed domains, on purpose, while working full-time.
Started as a DevOps engineer, building and managing cloud infrastructure on AWS and Azure with Terraform, configuring systems with Chef, running CI/CD pipelines through Jenkins, GitHub Actions, and Azure DevOps, and orchestrating containerized workloads with Docker and Kubernetes.
Then I started working with data teams and I saw 🔎 the gap immediately. Pipelines breaking silently.
- No versioning.
- No automated testing.
- No observability.
The same chaos DevOps had solved for software delivery, completely unsolved in the data world.
That's what pulled me in. I self-studied Apache Spark, Databricks, Python, and SQL while holding down my DevOps responsibilities — not because I was leaving infrastructure behind, but because I wanted to bring it with me into data engineering.
The automation mindset.The discipline of CI/CD. The obsession with reliability.
That intersection has a name: DataOps and that's exactly where I'm headed — building pipelines that are versioned, tested, monitored, and deployed the same way great software is.
AWS Solutions Architect Associate |
AWS Developer Associate |
Azure Administrator Associate |
Azure DevOps Engineer Expert |
HashiCorp Terraform Associate |
Certified Kubernetes Administrator (CKA) |
Bridging DevOps and Data Engineering to become a DataOps Engineer — automating, orchestrating, and operationalising data pipelines with the same rigour as production infrastructure.
I'm actively working on open source projects — feel free to check out my repositories and drop a ⭐ if something helps you!