This content originally appeared on DEV Community and was authored by Spot2
Building a commercial real estate platform like Spot2.mx requires more than just engineering skills: it demands strategic thinking, technical excellence, and the ability to navigate the complex intersection of business needs and growth.
As someone who has scaled development teams and architected systems that handle millions of property transactions, I want to share the core principles that have shaped our approach to building technology solutions that actually move the needle.
These insights come from years of leading engineering teams, making critical tech stack decisions, and understanding what it truly means to be a chief technology officer in today’s rapidly evolving landscape, where AI is gaining massive adoption.
What sets my view apart is the emphasis on practical, business-aligned innovation over hype: AI isn’t just a buzzword; it’s a tool we wield strategically to amplify human ingenuity, but only after rigorous evaluation to ensure it solves real problems without introducing unnecessary complexity.
Embrace the Full-Stack Mindset, but Master Your Domain
The Swiss Army Knife developer trap is real, but so is the opposite extreme: developers who refuse to understand how their piece fits into the larger puzzle. At Spot2.mx, we’ve found success with what I call “T-shaped expertise”: deep knowledge in your primary domain with enough breadth to collaborate effectively across the entire tech stack.
Our engineers, whether focused on crafting interfaces or working with backend services, go beyond writing code; they architect solutions with a deep understanding of system-wide interactions. They prioritize efficient caching, scalability, and modular abstraction to align with our operational needs.
JavaScript specialists ensure seamless API integration, crafting robust, maintainable code that adapts to evolving demands. Those optimizing SQL and backend logic focus on performance, minimizing latency and ensuring data integrity to enhance user experience, all while maintaining rigorous standards and precise scope management.
To elevate this further, our teams embrace Domain-Driven Design (DDD), where team structure mirrors business domains. This ensures that even as we cultivate generalists, engineers dominate their business understanding, providing solutions with crystal-clear user interactions, whether the “user” is a real person, an AI agent, or another system.
DDD helps us break down silos, fostering clarity in how domains communicate and integrate, which is crucial in a proptech environment where property data flows between search, matching, and transaction systems.
Approaching full-stack development at Spot2.mx isn’t just about knowing technologies; as time goes on, it’s vital to experiment with different methodologies and programming languages. But the real key is always anchoring everything to the business need: understanding why a feature exists before how it’s built. To cultivate T-shaped engineers, we prioritize:
Knowledge sharing: We rotate engineers across related teams. For instance, someone who built an API might join the team implementing it, ensuring hands-on transfer of expertise and reducing knowledge gaps.
Deep talks with product teams: Regular sessions to dissect user needs and brainstorm alternatives, ensuring technical solutions are user-centric.
Participate in debugging sessions/code reviews outside your primary domain: This builds empathy and broadens perspectives without diluting core expertise.
Attend infrastructure discussions: To amplify their vision of how everything interconnects, from cloud resources to data pipelines.
The software developer who understands the entire request lifecycle, from HTML rendering to database queries, makes better architectural decisions than someone who only knows their narrow slice.
Navigate Technology Adoption Like a Portfolio Manager
Technology decisions at scale require the same risk management principles as financial investments. We’ve seen too many software engineers chase every shiny framework, and we’ve also seen teams get stuck with legacy tech stacks that hold back innovation. Right now, with the explosion of new AI frameworks, it’s tempting to jump on every bandwagon, but as engineers, we need to master principles and tools rather than locking into opinionated approaches that might not fit our needs.
Our approach involves categorizing technologies into megatrends versus micro trends. IaaS and Gen-AI represent clear megatrends that deserve significant investment. Edge computing and micro-frontends are micro trends worth exploring through side projects and proof-of-concepts.
Strategic technology adoption framework:
70% proven technologies: Battle-tested tools that form your core tech stack.
20% emerging megatrends: Technologies with clear adoption trajectories.
10% experimental micro trends: High-risk, high-reward technical bets. Usually, what we do is evaluate these with different AI systems for deep research, investigating alternative solutions to improve operations. For example, instead of building a custom search engine from scratch with heavy optimizations, we leveraged a mix of Elasticsearch plus multi-layer caching to boost search performance across all services, delivering faster results without over-engineering.
Build Systems That Scale with Your Team, Not Just Your Users
Most programming languages and frameworks can handle user scale. The real challenge is building systems that scale with engineering team growth. When you go from 5 software engineers to 25, your development workflows, debugging processes, and deployment strategies need to evolve.
Organizational scaling considerations:
- Code architecture: Microservices vs. modular monoliths or something in between.
- Developer experience: Local development setup complexity.
- Testing strategy: Unit vs. integration vs. end-to-end coverage vs. regressions.
- Deployment pipeline: We invested heavily from the beginning of Spot2 into an automated pipeline that anyone on the team can use. Now, it’s common practice to leverage tools like CDK and Terraform to instrument and parameterize our infrastructure.}
- Monitoring and error reporting: How to catch and trace errors from one service through the full ecosystem of internal applications.
We learned this lesson the hard way. Our initial Laravel and Next.js applications worked beautifully for a small team, but as we added more software developers, merge conflicts and build times became productivity killers. We had to refactor both our code architecture and our development processes.
The tech lead role becomes critical here. You’re not just writing code; you’re designing systems that enable other software engineers to be productive.
When it comes to AI tools for code review and collaborative coding (what the industry called “vibe coding”), we’ve found them incredibly enabling in certain scenarios, but with clear boundaries. For proofs-of-concept (PoCs), whether for entirely new products or features within existing ones, we encourage their use to accelerate ideation and experimentation.
Tools like AI-powered code assistants can generate initial structures, suggest optimizations, or even debug tricky spots, freeing up human developers to focus on creative problem-solving. However, they’re not a free-for-all; we strictly adhere to established guides and rules in code style, architecture (like database design patterns), and security protocols.
In production code, AI suggestions undergo rigorous human review to avoid hallucinations or suboptimal patterns that could introduce debt. This balanced approach ensures AI amplifies our efficiency without compromising quality: it’s a spark for innovation, but grounded in our principles.
Develop Cross-Functional Intelligence
The CTO meaning has evolved significantly over the last years but more importantly over the last 12 months with the AI boom, technology and the way we can use it for creating more value has increased dramatically. Modern technology leadership requires understanding how engineering department decisions impact sales, product management, design, and marketing outcomes.
At Spot2.mx, our most successful initiatives happened when engineering collaborated closely with other functions. Our Gen-AI property matching algorithm succeeded because we involved sales teams in defining the problem and design teams in crafting the user experience.
Cross-functional collaboration strategies:
- Attend sales calls to understand real customer pain points.
- Participate in design reviews to grasp user experience implications.
- Join product management planning sessions to align technical possibilities with business needs.
- Collaborate with marketing teams to understand how technical capabilities translate to customer value.
This broad perspective has shaped our technology choices.
Master the Art of Technical Debt Management
Every software engineer deals with technical debt, but tech leads must develop strategies for managing it across entire systems. Technical debt isn’t inherently bad: it’s a tool that can accelerate development when used strategically.
Technical debt framework:
- Strategic debt: Intentional shortcuts to hit critical deadlines.
- Accidental debt: Poor decisions made without full context.
- Environmental debt: External changes that make current solutions suboptimal.
- Knowledge debt: Gaps in understanding that lead to poor implementations, or counterintuitively, overengineered solutions that ignore off-the-shelf providers. (This “build vs. buy” dilemma deserves its own full article, as it often sparks debates on efficiency and long-term ownership.)
We track technical debt using the same prioritization frameworks we use for features, like the Eisenhower matrix:
- High-impact, low-effort debt gets addressed quickly.
- High-impact, high-effort debt gets scheduled into sprint planning.
- Low-impact debt gets documented but might never be addressed.
The key insight: technical debt decisions should align with business priorities. Sometimes keeping that SQL query optimization on the backlog is the right choice if it frees up resources for customer-facing features.
Invest in Developer Productivity Infrastructure
The productivity multiplier of great development tooling is underestimated by most organizations. Small improvements in debugging workflows, testing infrastructure, and deployment automation compound over time.
Developer productivity investments that paid off:
- Automated testing pipelines: Reduced bug investigation time by 60%.
- Local development environments: Onboarding time dropped from days to hours.
- Monitoring and observability: Mean time to resolution improved by 40%.
- Documentation automation: Reduced support requests from other teams.
What we’ve found is that this is really useful with AI tools like Devin.ai, which can read full code, transform into documentation and provide answers to questions typically needed by developers who don’t know how to approach certain solutions.
We built custom tooling around our services setup that automatically generates API documentation from endpoints, consumable by other systems or AI agents. This investment in developer experience paid dividends in team velocity and code quality.
Modern big tech companies understand this principle. They invest heavily in internal tooling because developer productivity directly impacts product velocity.
Learn Continuously, but Learn Strategically
The pace of change in computer science and software development can be overwhelming. Coding bootcamps, online coding school programs, and the University of YouTube provide unprecedented access to learning resources, but without strategic focus, continuous learning becomes continuous distraction.
Strategic learning framework:
- Core competencies: Deep expertise in your primary programming languages and frameworks.
- Adjacent technologies: Broad understanding of complementary tools in your tech stack.
- Industry trends: Awareness of megatrends that could impact your domain.
- Soft skills: Public speaking, technical writing, and tech lead capabilities.
We encourage our team to learn through side projects that explore new technologies, but within guardrails that align with our strategic technology roadmap. A software engineer exploring edge deployment through a personal project might discover insights applicable to our commercial real estate platform performance optimization.
I read somewhere that writing is thinking, and we’ve internalized this: When someone learns or proposes something new, we use internal docs to document findings, solidifying and sharing the knowledge across the team.
Stack Overflow surveys and industry reports help identify which programming languages and frameworks are gaining traction, but the key is connecting those trends to your specific technical context. And to also bet on the waves we really want to dive into.
Build for Maintainability, Not Just Functionality
Code that works is the minimum bar. Code that can be maintained, extended, and debugged by different software engineers over time is the real challenge. This principle becomes critical as teams grow, and codebase ownership distributes across multiple developers.
Maintainability principles:
- Consistent coding standards and static analysis.
- Clear architectural boundaries and domain boundaries: Separation between front-end presentation logic and back-end business logic, with well-defined communication protocols so everyone understands how domains interact, through APIs, events, or shared models, ensuring smooth handoffs and integrations.
- Comprehensive documentation: Not just what the code does, but why architectural decisions were made.
- Testing strategy: Automating user journeys, integration testing, and regressions.
We’ve found that type safety caught integration errors that would have been runtime bugs, and IDE support improved developer productivity during debugging sessions.
The engineering department that prioritizes maintainability over shipping fast features often ships more features in the long term because they spend less time fighting technical debt and bug investigation.
Technology Leadership as a Craft
Being a tech lead or CTO in today’s environment requires balancing technical depth with strategic thinking. The proptech industry, like many sectors, is being transformed by Gen-AI capabilities and changing user expectations. Success comes from building systems that solve real problems while remaining adaptable to technological evolution.
The developers and tech leads who thrive are those who understand that technology choices are business decisions. Every framework selection, programming language decision, and architectural choice should connect to measurable outcomes for users and stakeholders.
Whether you’re working at a commercial real estate platform like Spot2.mx, a big tech company, or a startup, these principles scale across different contexts and company sizes.
This content originally appeared on DEV Community and was authored by Spot2