In a session hosted by The Top Voices, platform engineer Ivan Timonov shared why short-term solutions in data infrastructure often become long-term liabilities — and what teams can do to prevent that. Drawing on real startup patterns, he unpacked how these systems evolve, where hidden costs accumulate, and which architectural principles help platforms scale sustainably.
Speaker
Ivan Timonov is an MLOps Engineer at Tabby, a UAE-based BNPL fintech unicorn. He works on optimizing cloud infrastructure, developing FinOps practices, and building systems that help teams gain better visibility and control over cloud costs.
From Quick Fix to Critical Infrastructure
Most data systems start as quick workarounds — a SQL query here, a bash script there — with the intention to “clean it up later.” But “later” rarely comes. If the system works, it sticks. Over time, it becomes the core for analytics, reporting, even ML features. These systems become harder to change, harder to onboard new people into, and increasingly fragile.
The Four Layers of Hidden Cost
The cost doesn’t show up early, but it grows as the company scales:
- Operational overhead: Manual fixes, slow migrations, and over-reliance on a few key engineers.
- Data quality issues: Copy-paste logic and inconsistent metrics erode trust.
- Scalability limits: What worked for 2–3 people breaks down with 10–15. Every change becomes a risky project.
- Risk and compliance: No audit trails, unclear ownership, no enforcement of access or data policies.
A Maturity Model for Data Platforms
A simple framework helps evaluate how ready a platform is to scale:
- Level 0: UI and cron-based jobs, no global visibility.
- Level 1: Scripts and CLI with local conventions.
- Level 2: Declarative specs and API-based workflows.
- Level 3: Policies as code and automated governance.
Principles for Scalable Systems
To break out of the patchwork trap, Ivan emphasized six key principles:
- API-first: Use UI for exploration, APIs for production systems.
- Spec-driven: Each job has a single declarative spec — the source of truth.
- Templates over duplication: Avoid slight variations by using shared templates with defined parameters.
- Validation and dry runs: Run checks on every change before it hits production.
- Observability by default: Include logs, metrics, and alerts from the start.
- Automated governance: Bake in budget limits, labels, and access rules into CI/CD — not spreadsheets.
Conclusion
Temporary systems almost always become permanent. If left unmanaged, they accumulate invisible costs that slow teams down and increase risk. But even without large tools or teams, startups can move toward scalable platforms. Declarative job specs, built-in checks, and lightweight observability go a long way toward reducing failure, improving trust, and helping platforms grow without breaking.