The Engineering Ethos
We, as engineers, are inculcated with a near-sacred duty towards ‘good code‘. It’s the catechism recited in standups, the virtue signaled in code reviews, the bedrock upon which we build careers and reputations. We aspire to architectures that scale asymptotically, codebases resilient to the entropy of time and changing requirements, systems that handle edge cases lesser minds might overlook. We are the architects of digital cathedrals, the 10x developers, the unicorns immune to the gravitational pull of mediocrity. We take pride in doing it right.
Questioning Perfectionism
But is this pride always justified? Is this meticulous care universally applicable, or can it become a gilded cage, trapping us in pursuit of an elegance the situation neither demands nor rewards? Can we, perhaps, afford not to be perfect?
Contrasting Contexts
My own journey spans contexts demanding wildly different engineering philosophies. Architecting trading systems for hedge funds[^1], where milliseconds count and a single uncaught exception can evaporate millions, necessitates a level of rigor bordering on paranoia. Stability, predictability, and correctness are non-negotiable table stakes. Time spent meticulously crafting robust, fault-tolerant systems isn’t just warranted; it’s the only responsible path.
The One-Off Scenario
Contrast this with a hypothetical, yet common, scenario: building a data ingestion pipeline tasked with processing terabytes of data, but destined for only a single execution. Perhaps it’s a one-time migration, an initial data load for an ML model, or a historical analysis. What virtues should we optimize for here? The timeless elegance of the codebase? Its long-term maintainability? Or the raw speed of delivery and successful completion of the one-off task?
The Counterargument
The seasoned engineer, scarred by experience, immediately raises a valid objection: “No script is ever truly one-off. No module too insignificant to escape future entanglement. No feature exists in a vacuum devoid of maintenance.” And there’s truth to this. Systems evolve in unpredictable ways; throwaway scripts become critical dependencies; quick hacks accrete into immovable technical debt. We’ve all seen it. We know better than to consciously create messes.
The Opportunity Cost
Yet, is the potential for future reuse a sufficient reason to invest heavily in upfront architectural perfection when the immediate, tangible cost is delay? This is where the calculus becomes complex, where the dogma of “good code” falters. We must weigh the opportunity cost – the value lost by delaying delivery, the features unbuilt, the market share conceded while we polish code for a future that may never arrive – against the potential future cost of instability or rework.
The Complexity Trap
The insidious trap, especially for those of us who genuinely enjoy the craft, is defaulting to complexity. I confess, my initial instinct for even moderately complex tasks often drifts towards decoupled microservices, message queues (SQS), state machines (Step Functions), and resilient data stores – architectures designed for longevity and scale. Elegant? Yes. Necessary for a task achievable within a Jupyter notebook or a simple script? Often, demonstrably not. It’s easy to get lost in the satisfying puzzle of good architecture and lose sight of the actual objective: delivering value now.
Contextual Optimization
This isn’t an endorsement of deliberate sloppiness or writing code that simply doesn’t work. It’s an argument for contextual optimization. It’s about recognizing that “good” is not an absolute, but a spectrum defined by constraints: time, budget, expected lifespan, criticality, and the ever-present opportunity cost. The ability to navigate this nuance, to discern when “good enough” truly is good enough, distinguishes the pragmatic engineer valued by the business from the purist who delivers exquisitely crafted solutions too late to matter.
The Path Forward
The path forward often involves embracing simplicity initially. Start with the minimum viable solution. Observe real-world usage patterns, data flows, and failure modes. Let the evidence of repeated use or evolving requirements justify subsequent investment in robustness and scalability. Productize when the need is clear and present, not merely hypothetical. Resist the siren song of premature optimization and architectural astronautics.
True Engineering Excellence
Ultimately, engineering excellence isn’t solely about adherence to abstract principles of code quality. It’s about the wisdom to apply the right principles, at the right time, to achieve the right outcome within the given constraints. Sometimes, that means having the courage, and the judgment, to write ‘bad’ code for all the right reasons.