Viewpoint by Jonathan T., Senior Consultant at Amaris Consulting
Among Picasso’s many talents was his gift for simplifying the complex into something instantly recognizable. That mindset, cutting through noise to get to what matters, holds valuable lessons for data engineering.
These projects often launch with big promises: ROI, optimized budgets, cutting-edge solutions. But somewhere between the blueprint and the build, costs spiral, systems bloat, and “smart” becomes “over-smart.” The result? Solutions that outpace their purpose and your budget.
According to CodeScene, many teams get the output of 75 developers while paying for 100, thanks to technical debt. Data engineering is no exception. More resources don’t fix misdirection. The key isn’t just building, it’s building right. That means avoiding both over- and under-engineering.
When “just in case” becomes the default
Design sets the foundation—choosing the right tools, mapping data flows, ensuring security, and planning integration. Typically led by architects, this phase is where ambition can quietly tip into excess.
Take a customer analytics pipeline. As soon as a website enters the mix, teams often default to real-time processing, microservices, and top-tier encryption. But not every use case needs this level of complexity. Batch processing might be sufficient. Simple ETL tools can outperform layered services. Traditional star schemas still work. And while data security is critical, not all datasets require military-grade encryption.
When in doubt, assess risk realistically. Over-engineering should never be the default.
Complexity for complexity’s sake
Even with a lean design, development can derail progress. Here’s how: Imagine the same company, same project. This time, design is sensible, but implementation isn’t.
To “future-proof” the system, we over-parallelize, even when data volume doesn’t require it. We build complex retry logic for rare errors. We avoid indexes to avoid looking junior. We write convoluted queries because they feel more advanced.
Uncertain about cloud scaling, we build custom monitoring with three tools. We refactor everything to be AI-agent compliant and tie it all to dozens of dependencies—just in case.
This is over-engineering in another disguise: unnecessary complexity, premature optimization, and bloated systems that are expensive to maintain. It’s like using a GPS to find your way around your own living room.
Two sides of the same coin
Over-engineering isn’t always a choice, it’s often a reaction. A common trigger? Poor data quality. According to Gartner, bad data costs companies an average of $12.9 million annually. To compensate, teams patch and build around flaws, creating solutions disconnected from the actual problem.
Add in regulations like GDPR, siloed systems, unclear data ownership, and limited resources, and suddenly, complexity seems like the only path forward.
But it’s a trap. Under pressure to “just make it work,” teams rush. And that rush leads to both over- and under-engineering: band-aid fixes on one end, critical oversights on the other. The result? Costly errors. Think of the ride-sharing company that miscalculated driver payments—millions lost due to bad data pipelines.
Cognitive traps and misplaced confidence
One of the trickiest things about over-engineering is that it often feels like the right thing to do. As Marshall McLuhan once put it, “We don’t know who discovered water, but we know it wasn’t a fish.” When you’re immersed in a complex system, it’s hard to recognize unnecessary complexity.
So why do we fall into this trap? Sometimes it’s the fear of future scalability issues or the pursuit of perfection. Other times, it’s the pressure to future-proof systems, the fear of missing out on the latest technology, or a tendency to imitate Big Tech practices without considering context. The need to integrate complex systems, manage licensing costs, or attract and retain top talent can also nudge teams toward unnecessarily sophisticated solutions.
We also get caught in biases: the planning fallacy (underestimating time), or overconfidence (overestimating capability). It’s easy to forget a core principle of software engineering: iterate.
If you hear, “We can’t afford to fail in production,” you’re likely dealing with an under-engineered foundation. Fixing that can unlock efficiency across the board.
Ultimately, retrospectives should be honest. Strategy teams and operational teams must share accountability. Most methodologies, agile or not, rely on iterations to move forward. And iteration is one of the best antidotes to over-engineering.
The price of complexity
Over-engineering isn’t just a technical issue, it’s a business risk. Here’s what it often leads to:
- Maintenance overload
- System fragility and failure risk
- Slower development cycles
- Inflated infrastructure costs
- Long onboarding ramps
- Delayed time-to-market
- Budget uncertainty and hidden costs
- Developer frustration and burnout
- Revenue loss from bad data usability
In tech-heavy organizations, technical debt is a known enemy. Over-engineering is its close cousin and it’s just as damaging.
How to avoid it: A practical checklist
Here’s how to keep your systems lean, maintainable, and fit-for-purpose:
1. Start simple
2. Validate the real problem first
3. Pick tools that fit, not just the trendiest
4. Don’t skip audits or retrospectives
5. Leverage AI smartly, don’t overhaul for it
6. Prioritize maintainability over novelty
7. Keep asking: What problem are we solving?
8. Monitor, monitor and monitor again
Data engineers already spend 40% of their time on data quality, with each incident taking around 13 hours to resolve, according to Monte Carlo Data’s 2022 survey. Half of companies rely on customer complaints to detect issues. That’s a red flag.
Imagine having full visibility over your codebase: complexity metrics, build times, unused services, failure recovery, cost hotspots. Imagine real-time insights into security, lineage, access control, audit trails. That’s where modern monitoring makes the difference, it exposes both direct and indirect costs.
Simple doesn’t mean simplistic
This article could just as easily be a manual for creating technical debt—and that’s precisely the point. Over-engineering doesn’t come from carelessness; it often comes from good intentions taken too far. But discipline, not complexity, is what builds sustainable systems.
Simplicity isn’t about cutting corners. It’s about understanding what really matters, solving the right problems, and making deliberate, thoughtful choices. The best systems aren’t the ones packed with the most features or frameworks—they’re the ones that evolve from lean, well-functioning foundations.
In the end, over-engineering is just another form of waste. Its cure lies in clarity of purpose, the confidence to start small, and the willingness to iterate. Because in data engineering, the simplest solution is often the one that scales best—and costs the least in the long run.
Curious how our Data & AI Center of Excellence helps companies reduce tech debt and design with purpose? Learn more about our Data & AI Center of Excellence here.
