Cloud delivery workflows
Reusable delivery workflows for Python services and Java/Scala workloads, with federated cloud authentication.
The system at a glance.
A conceptual view of the flow and its boundaries.
The challenge.
Independent services need consistent packaging, infrastructure deployment, environment separation, and release traceability without duplicating every pipeline.
My contribution.
Owned Databricks deployment templates and worked across reusable service delivery workflows and CI/CD modernization.
Implementation.
- Create Java/Scala build templates supporting Maven or Gradle, S3 JAR/configuration publication, and Databricks job creation or updates.
- Use GitHub-to-AWS OIDC and environment-specific IAM roles for temporary cloud credentials.
- Provide reusable Python workflows for unit testing, packaging, global/regional OpenTofu deployment, and post-deployment acceptance testing.
- Support manual production promotion, configuration backups, and release tags in the Databricks template path.
Design decisions.
Reusable workflows standardize delivery but require stable input contracts and careful version management.
Build, application validation and security review remain separate responsibilities; each workflow needs an explicit contract.
Validation.
Validation varies by workflow: Python services use unit and acceptance tests; JVM templates validate build and package creation.
The outcome.
Provided repeatable delivery patterns for both serverless services and JVM-based data workloads.
Explore the pattern.
The companion repository is an independent educational demonstration built with synthetic examples. It illustrates related engineering patterns and does not contain employer source code or production data. Repositories are currently private; please contact me for access.
Open private GitHub demo