Building a Data Platform Roadmap for a Swiss Startup
No code was deployed. We analysed the existing AWS infrastructure, documented architecture decisions, and created an actionable backlog, giving the team clarity before committing to execution.
Tags: AWS, Data Platform, Advisory, Architecture, Snowflake, Backlog as Code
Context
A Swiss startup had grown its data infrastructure organically over several years. Multiple AWS accounts, each managed differently. Infrastructure deployed through a mix of CloudFormation, Terraform, and manual setup. Technical debt claims from the engineering team, but no structured validation of what was real and what was assumed. Before investing in execution, they needed an independent assessment.
The engagement: advisory, not execution
CloudBoostUP was brought in purely in an advisory capacity. No infrastructure was modified. No code was deployed. The scope was deliberately constrained: analyse the current state across all AWS accounts, validate technical debt claims against live infrastructure data, document findings as structured architecture decisions, and translate everything into an actionable backlog the team could execute independently.
What we delivered
Three Pillars of Clarity
Advisory deliverables
- AWS API validation of technical debt
- Cross-account security audit
- Network architecture review
- Cost and risk assessment
- Structured decision records
- Current-state diagrams
- Gap analysis per component
- Recommended target architecture
- Prioritised Jira stories
- Acceptance criteria per story
- Dependency mapping
- Execution-ready for the team
Every finding was validated against live AWS data; no assumptions, no guesswork. The backlog gave the team a clear path from assessment to execution.
The infrastructure analysis went beyond surface-level review. Each technical debt claim was validated by querying AWS APIs directly, verifying VPC configurations, IAM policies, security group rules, and resource states across accounts. Claims that proved inaccurate were corrected with evidence. Claims that were valid were documented with the specific data that confirmed them.
The architecture documentation was created as structured decision records in Confluence, following a consistent format: context, decision, consequences, and status. This gave the team not just a snapshot of the current state, but a reasoning framework they could extend as they made future decisions.
The backlog translated findings into executable work. Each story included clear acceptance criteria, dependencies on other stories, and enough technical context for the engineering team to pick up and deliver without further clarification.
Outcome
Before: Before Technical debt claims without evidence No structured architecture documentation No prioritised execution plan Decisions made on assumptions Team unsure where to start
After: After Every claim validated against live AWS data Structured decision records in Confluence Prioritised backlog with acceptance criteria Decisions grounded in infrastructure evidence Clear execution path the team owns
This is what our Advisory plan is designed for. When you need clarity before committing to execution: an independent assessment, structured documentation, and an actionable roadmap. Advisory gives you exactly that, without long-term commitments.
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