CI/CD Workflows and Scalable Kubernetes Deployments

Summary

Contributed to the design and fully maintained a comprehensive CI/CD and infrastructure management setup tailored for data platform development and deployment. Implemented automated GitHub Actions workflows for Docker image builds, DBT testing, and Terraform validation, ensuring consistent and reliable deployments across multiple environments. Managed Kubernetes clusters using Helm and transitioned parts of the Infrastructure as Code from Permifrost to Terraform.

Context & Challenge 🧩

The architecture supported isolated environments—production, beta, and release/staging—each running containerized tools like Airflow, Meltano, DBT, and Terraform on Kubernetes (AKS). Deployments were managed via Helm charts, with custom configs for secrets, connections, and runtime parameters.

The tooling had to balance rapid development with cross-environment stability. With infrastructure partly managed via Permifrost, a careful, non-disruptive migration to Terraform was underway. Reliable, testable deployments were critical to avoid regressions and support agility.

My Role & Contributions 🧑‍💻

My focus was on evolving and maintaining the platform, keeping it scalable, clean, and resilient as both infrastructure and tooling evolved. Key contributions included:

CI/CD Automation (GitHub Actions) -> Built and maintained pipelines for Docker image builds, DBT model validation, and Terraform plan checks, streamlining deployment workflows.

Multi-Environment Image Management -> Managed Docker images per environment, reducing duplication and ensuring consistent releases across staging, beta, and production.

Helm-Based Kubernetes Deployments -> Oversaw templated, consistent Helm deployments for services like Airflow, enabling smooth upgrades and rollbacks.

Terraform Infrastructure Migration -> Gradually transitioned from Permifrost to Terraform with automated validation to ensure safe, reliable infrastructure changes.

Operational Refactoring -> Continuously improved platform components for scalability, stability, and long-term maintainability.

Outcomes & Learnings 🚀

This work contributed to a scalable, testable, and automation-friendly data platform that:

Supports multi-environment orchestration for Airflow, Meltano, and DBT.

Automates deployment and testing of data pipelines and infrastructure.

Maintains environment separation and reduces release risk.

Enables infrastructure updates with confidence using Terraform and CI validation.

Encourages agile iteration while supporting long-term system health.

This ongoing experience deepened my expertise in Kubernetes orchestration, infrastructure as code, and CI/CD automation in data engineering ecosystems.