From Engineer to Architect: The CUBE Method for High-Impact Answers

You’re in the final interview for a Senior Platform Engineer role. You’re at the whiteboard. The CTO leans forward and asks, "How would you design our observability strategy from scratch?"

You know the tools. You've done this before. Confidently, you dive straight in : "I'd use Prometheus for metrics, Loki for logs because it's cost-effective, OpenTelemetry for tracing...".

The CTO cuts you off. "I don't need a list of tools. I asked for a strategy."

At this moment, you realise that something is missing. You don't need more knowledge, you need a way to organise and structure this knowledge and produce your response. They want to see how you think, about trade-offs, business value, about risk. After strugling with this myself, I am proposing a mental framework called the CUBE method, ensuring you deliver architect-level answers every time.

(Disclaimer: I am not yet an architect myself, but it is my career goal, and I am actively working toward it. This post aims to share strategies I have found useful on my journey.)

Why a Mental Framework ?

In a high-pressure situation, our brain tends to grab the most familiar information first : the technical details. We dive into a rabit hole of technologies and application, and we can loose track of the purpose, the business aspect, long-term maintenance burden... A mental framework acts as a checklist, a structured algorithm that guides our thinking.

It makes you pause and ponder, zoom out, analyse a problem from multiple critical angles before speaking. This helps us craft a more complete solution, but als structures our communication, making our answer logical, persuasive, and have a clear progression.

The CUBE Method Exlpained

CUBE is an acronym for four pillars of architectural thinking. Before answering any design question, we can mentally run through these four filters.

C - Cost & Business Impact

Technology serves the business. An architect's first question should be about value.

• Cost: What is the Total Cost of Ownership (TCO) ? Is there an upfront investment vs. long-term operational cost?
• Business Value: How does this choice accelerate the Time-to-Market? Does it reduce operational overhead (MTTR, etc.)? Will it make us save money?

U - Upkeep & Usability

A brilliant solution that is impossible to maintain is a liability. We need to consider the human factor.

• Maintainability: How will we monitor, debug and observe this system ?
• Developer Experience (DX): How does this choice affect our developpers' daily lives ? Does it reduce their cognitive load or add to it ?
• Disaster Recovery: What is our recovery plan when it fails ?

B - Bulletproof security

Security is not a feature, it's a prerequisite. Think like the bad guy.

• Attack Surface: Does this increase or decrease our exposure ?
• AuthN/AuthZ: How do we manage identity and permissions ?
• Secrets & Compliance: How are secrets, keys and certificates managed ? Do we meet compliance standards like GDPR ?

E - Elasticity & Efficiency

The system must be resilient, performant and scalable under real-word conditions.

• Scalability: How does it handle load? What are its bottlenecks ?
• Reliability: Is it fault-tolerant? What are its single points of failure?
• Performance: What are the latency and throughput characteristics?

Applying CUBE: A Real-Word Example

Let's go back to our CTO's question:

'What is your minimal, viable observability strategy for a new, high-stakes project with a tight budget?'

Here's how we answer using the CUBE framework to structure our thinking :

'That's a critical question. My strategy wouldn't be to adopt every tool at once, but to layer our capabilities based on the highest vaue-to-cost ration. I'd approach it in three pragmatic phases :

Phase 1: Foundational Reliability with Metrics

Our first priority is answering the question, "Is the system online and healthy?"

• Therefore, my Day 1 action is setting up Metrics with Prometheus and Grafana. It gives us a real-time 'electrocardiogram' of the system (CPU/RAM, latency, request rates). This is our baseline for (E) Reliability and lets us create proactive alerts. It's the cheapest and fastest way to mitigate the (C) Business Cost of a total outage because we can see a problem before our customers do.

Phase 2: Efficient Debugging with Logs

Once an alert fires, we need to answer, "Why did this happen?"

• This is where Logs are essential. To manage (C) Costs, I would implement centralized logging with Loki, not ELK. Loki's architecture is much cheaper to run. This directly improves our (U) Upkeep by giving developers the context they need to debug failures quickly, improving our MTTR without a massive infrastructure investment.

Phase 3: Surgical Security & Performance Audits with Tracing

Finally, once the system is stable, we need to answer complex questions like, "Where is the bottleneck?" or "Did that request follow an authorized path?"

• This is the role of Distributed Tracing with OpenTelemetry.
• The (C) Cost of tracing is high, so we control it by implementing aggressive sampling (e.g., recording only 1 in 1,000 requests).
• Its primary value is twofold: for deep (E) Performance analysis to optimize slow transactions, and for (B) Bulletproof Security. A full trace provides an immutable audit trail, allowing us to perform forensic analysis after a security incident or detect anomalous, unauthorized interactions between services. We introduce this last because its value shines brightest once we have a stable and scaling system.

The Result

By structuring our answer this way, we did several things:

• We answered the strategic question, not just the technical one.
• We demonstrated a pragmatic, cost-conscious mindset.
• We justified every choice with a clear trade-off.

We did not just give a right answer. We proved we think just like an architect. And this time, the meticulous CTO simply nods, impressed.