Are You Growing as an Engineer—or Just Closing Tickets?

The Question That Never Appears in the Sprint

Some weeks, you complete ten tickets, reduce the backlog, and join every daily stand-up with an impressive list of accomplishments. From the outside, everything suggests that you are growing. Yet, at the end of the sprint, an uncomfortable question may remain: Am I becoming a better engineer, or have I simply learned to work faster?

The difference is not always reflected in Jira, pull request counts, or logged hours. Growth in software engineering means developing judgment, understanding problems more deeply, and improving the quality of the decisions you make before writing a single line of code.

Completing More Tickets Does Not Always Mean Moving Forward

Tickets are necessary. They organize work, distribute responsibilities, and turn product needs into concrete actions. The problem begins when they become the only measure of an engineer’s contribution.

You can complete a large number of tasks without understanding the system, questioning decisions, or participating in problem definition. You can also close fewer tickets while eliminating a recurring source of failure, improving an architecture, or helping the team make a decision that prevents months of rework.

The Shift May Look Like This

  • Before: You receive a predefined solution and implement it.
  • After: You investigate the problem and propose alternatives.
  • Before: You fix the same type of issue repeatedly.
  • After: You identify and eliminate the root cause.
  • Before: You optimize only your own delivery.
  • After: You improve the entire team’s ability to deliver.

Sign 1: You Are Asking Better Questions

A growing engineer does not necessarily have every answer. They have better questions. Before beginning a task, they ask who needs the functionality, what behavior is expected, which constraints exist, and what might happen when the system scales.

As you grow, you stop asking only, “How should I implement this?” and begin asking, “Why are we building this?”, “What risk are we accepting?” and “Is there a simpler solution?” That transition shows that you are moving from executing instructions to exercising technical judgment.

Sign 2: You Understand the Impact, Not Just the Code

Software development creates value when it solves real problems. An API is not simply a collection of endpoints. It may be the infrastructure that allows someone to access healthcare, complete a payment, manage their finances, or find a professional opportunity.

Growth means understanding the connection between code, product, and user. When you understand the desired outcome, you can make better decisions about performance, accessibility, security, maintainability, and user experience. Your code stops being an isolated deliverable and becomes an intentional part of the product.

Sign 3: You Think in Systems and Consequences

Speed may make you efficient, but systems thinking makes you dependable. A more mature engineer considers how a change may affect other layers of the application, which dependencies it introduces, and what its operational cost could be six months from now.

Technical maturity also requires recognizing trade-offs. There is no perfect architecture and no technology that is right for every context. There is only a decision that is appropriate for a specific problem, supported by clear assumptions and a thoughtful understanding of its consequences.

Growing as an Engineer in the Age of AI

This distinction has become even more important in an environment where producing code is increasingly fast. AI tools can accelerate implementation, documentation, testing, and debugging, but they do not automatically provide product understanding, architectural judgment, or accountability.

Producing more code does not guarantee that teams are building better systems. The real advantage appears when engineers use technology to strengthen their capabilities: reducing repetitive work, testing assumptions faster, improving feedback loops, and dedicating more attention to the decisions that require human context.

Community Accelerates the Growth Tickets Cannot Measure

Nobody becomes an exceptional engineer entirely on their own. Pair programming, code reviews, mentoring, and architecture conversations reveal blind spots that rarely become visible when the only goal is completing a task.

A developer may remember for years the person who taught them to investigate an incident without looking for someone to blame, justify a technical decision, or write code review feedback with empathy. That kind of learning strengthens a developer community and creates a technology culture in which knowledge is not accumulated by a few people. It circulates.

Lessons That Demonstrate Real Growth

  • Explain a decision without hiding behind technical jargon.
  • Receive feedback without turning the conversation into a personal defense.
  • Review code in search of clarity, not superiority.
  • Document knowledge to reduce dependency on specific individuals.
  • Share context before presenting conclusions.

Technical Leadership Begins Before the Title

Technical leadership does not begin when someone receives the title of Tech Lead. It begins when a person improves the quality of team conversations, shares relevant information, and helps others move forward without trying to control every decision.

It also appears when someone acknowledges that they do not know something, asks for support early, or protects the team from a seemingly fast solution that could create unnecessary technical debt. Leadership is not proving that you are the smartest person in the meeting. It is increasing the collective capability of the team.

The Strength of LATAM Talent in Global Technology

LATAM talent is participating in a global technology industry that continues to expand rapidly. Across the region, engineers are contributing to open-source ecosystems, developing global platforms, scaling infrastructure, and solving complex problems for organizations around the world.

This momentum did not appear overnight. The continued growth of developer communities in countries such as Brazil, Colombia, Argentina, Mexico, Costa Rica, Chile, Peru, and Guatemala demonstrates that software development in Latin America is not a peripheral trend. It is becoming an increasingly important force within the global technology economy.

Nearshore Should Not Mean Working From the Periphery

For years, part of the conversation around nearshore software development focused primarily on cost efficiencies, compatible time zones, and operational convenience. While these factors remain relevant, they do not fully explain the value that Latin American engineers bring to international products.

Global teams do not simply need professionals who are available during convenient working hours. They need engineers who can understand complex contexts, communicate clearly, challenge assumptions, adapt quickly, and take ownership of outcomes. That is where the creativity, resilience, and continuous learning demonstrated by LATAM talent become a strategic advantage.

How to Measure Growth Without Relying on the Backlog

To determine whether you are growing, observe the problems you can now address, the conversations in which you participate, and the amount of context you need to make a responsible decision. Consider whether your work reduces uncertainty, makes other people more effective, and improves the long-term sustainability of the system.

A valuable practice is to review your progress every quarter. Do not measure only the technologies you learned or the tickets you completed. Ask yourself which decisions you can make today that you would have delegated six months ago, which mistakes you have stopped repeating, and how many people you helped develop a new skill.

Technical Growth Checklist

  • Do I understand the business behind the product more clearly?
  • Can I explain the trade-offs behind my decisions?
  • Do I identify risks before they become incidents?
  • Do I help other developers work more effectively?
  • Am I solving increasingly ambiguous problems?
  • Does my work leave the system in a better condition?

Building a Culture Where Engineers Can Grow

At Mismo, we believe a career in engineering should not become an endless sequence of tasks. Growth happens when engineers from different countries can share experiences, collaborate with global teams, and contribute to products whose impact extends far beyond their own region.

In a culture built around genuine collaboration, developers feel valued not only for what they deliver, but also for the questions they ask, the knowledge they share, and the way they help others grow. That is the kind of community we continue to strengthen: close, diverse, technically ambitious, and deeply human.

We Are Building What Comes Next

The future of engineering will not necessarily belong to those who generate the most lines of code or close the most tickets. It will belong to those who can transform technology into responsible solutions, understand complex systems, collaborate across differences, and continue learning even as tools and frameworks change.

We are a generation of developers across Latin America building the future from different cities, cultures, and experiences. Growth means recognizing that power, sharing it with others, and using every project not only to deliver software, but to become the engineers the global technology industry needs.

When Intuition Meets Data: Using Analytics to Make Better Decisions

How data strengthens collaboration across teams

At Mismo, engineers, operations teams, and recruiters make decisions every day that impact delivery, growth, and long-term results. In this context, intuition is still important, but relying solely on it is no longer enough.

Every hiring decision, role change, resignation, project milestone, ticket resolution, or system deployment generates data that reflects how our teams actually work. Collecting this data is only the first step — what really matters is understanding it and using it intentionally to guide decisions across different clients and delivery models.

Because our teams operate with a high degree of autonomy and are constantly adapting to changing client needs, priorities, and technical challenges, decisions based mainly on assumptions can easily create misalignment. Clear and reliable data helps bring everyone back to the same page by creating a shared, data-driven perspective that complements intuition with real evidence and context.

When information is visible and easy to compare, recruiters, engineers, and leaders can work from the same understanding. This makes it easier to spot patterns, identify bottlenecks, and see how decisions affect hiring speed, delivery timelines, team stability, and overall results. It also surfaces insights that are often missed in day-to-day work — such as where candidates drop off in hiring processes, which roles take longer to fill, or when engagement begins to decline.

This is where people analytics comes in: it is often associated only with HR, but in reality it supports everyday decision-making across teams, especially in environments where delivery, timelines, and team continuity are critical. The process itself is not complex — data is collected, cleaned, analyzed, visualized, and shared — but its value depends on consistency, accuracy, and careful interpretation.

When data is incomplete or unreliable, decisions can be affected, leading to hiring mistakes, budget issues, or retention problems, particularly in multi-client environments with shifting priorities. This is why it is important to validate information, review multiple sources, and question anything that does not fully make sense.

With clearer visibility into how teams operate, engineers, recruiters, and leaders can ask better questions, align earlier, reduce friction, and make adjustments based on data rather than assumptions — while still preserving the autonomy needed to adapt to different clients and contexts.

When data starts telling the right story to the right people

Data only becomes useful when people can actually understand it. Raw numbers by themselves usually do not say much. What makes the difference is how that information is shared and explained, especially when insights are presented to managers or clients.

Telling a story with data does not mean showing everything that is available. In practice, it is more about choosing what is relevant and keeping the message simple. Clear visuals, short titles, and a logical order help people follow the information, understand why it matters, and decide what to do next. When data is structured this way, conversations tend to be more focused and productive.

This is especially noticeable when sharing results with managers or clients. Looking at trends over time, like hiring progress or delivery stability, helps move the conversation away from isolated situations and toward a broader view of what is happening. With that context, teams can talk about impact, risks, and next steps without focusing only on single data points.

Using data this way also helps build trust. When information is consistent, easy to follow, and clearly linked to real outcomes, managers and clients feel more confident about the decisions being made. In fast-moving environments, this clarity often makes the difference between simply reviewing data and actually acting on it.

Driving impact through People Analytics: from recruitment to workforce decisions

People analytics helps turn data into insights that support better decisions across the organization. In tech recruiting, reviewing the candidate funnel can highlight where talent is being lost and whether expectations match reality. Tracking time-to-hire makes delays easier to see and shows how they affect engineering teams. Looking at sourcing channels also helps identify which pipelines consistently bring strong candidates.

This kind of insight improves transparency and strengthens alignment between recruiters, hiring managers, and technical teams. It also helps create better conversations, focused on improvement instead of assigning blame.

Over time, it becomes clear that people analytics is not only useful for recruitment. Looking at engagement patterns can help teams spot retention risks earlier and take action before issues grow. DEI data can also bring visibility to potential biases in hiring, promotions, or compensation, helping teams have more honest conversations based on facts rather than assumptions.

Learning and development data makes it easier to see whether training initiatives are actually helping people grow and develop new skills and whether they stay motivated and connected to the organization. The same applies to performance and potential data, which often supports decisions around promotions, succession planning, and long-term talent development. Compensation data also plays an important role in maintaining fairness, staying competitive, and improving retention.

When this information is connected across recruitment, engagement, development, and workforce planning, decision-making becomes clearer. Teams collaborate more easily, processes improve gradually, and goals feel more shared. Instead of relying on assumptions, decisions are guided by data that supports real action and meaningful impact.

Analytics as a personal skill: using data to reflect and improve

Analytics is not only something used by teams or leaders. It can also be helpful at an individual level, especially when trying to better understand how you work and where your time and energy go. Looking at patterns over time can highlight small changes that actually make a difference, show where assumptions influence decisions, and point out opportunities to improve everyday processes.

For me, the most important part is using data as a way to reflect, not to judge yourself or compare yourself with others. Simple things like how long it takes to solve issues, how quickly you respond to internal or client requests, or how much time is saved by automating repetitive tasks already say a lot. Feedback also plays a big role here, especially when you take the time to reflect on it and turn it into small improvements.

Treating analytics as a personal skill helped me focus on continuous improvement rather than perfection. Improving day-to-day performance has a direct impact on clients, and better client experiences often lead to more motivated teams. Over time, this creates a healthier cycle of learning, improvement, and shared results.

Bibliography

  • HRissan. (2025). People Analytics Diploma [Online training program]. HRissan.

Written by:

María Luján Ciommo
IT Recruiter
Country: Argentina