Careers Blog, Engineering, Women in STEM

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

Employer Blog

How to Hire Great Engineers in the Age of LLMs

A practical playbook for modern engineering leaders

Not long ago, hiring an engineer was relatively predictable.

You gave candidates a take-home project.
You reviewed their repository.
You looked for clean architecture, thoughtful test coverage, and signs that they could work independently.

That process worked because writing production-quality code required time, repetition, and experience. The output itself was the signal.

Today, that signal is broken.

A well-prompted AI agent can complete what used to be a two-week take-home assignment in minutes. Boilerplate is instant. Scaffolding is automatic. Even complex integrations can be generated on demand.

So the hiring question has fundamentally changed.

It is no longer:

“Can this person write good code?”

It is now:

“Can this person think clearly, make good decisions, and deliver real outcomes in an AI-native environment?”

That shift is forcing every CTO, VP of Engineering, and founder to redesign how they evaluate talent.

The Big Shift: Code Output Is No Longer the Primary Signal

In the pre-LLM world, reviewing code told you almost everything you needed to know. The structure of a project reflected how someone thought. The way they handled edge cases showed their experience. Their test strategy revealed their maturity.

Now two candidates can submit nearly identical solutions.

One deeply understands the system they built.
The other simply accepted what an AI generated.

If you evaluate only the output, you cannot tell the difference.

That is why the strongest engineering organizations have moved their interviews away from static artifacts and toward dynamic observation. They are no longer trying to measure how fast someone types or how much syntax they remember. They are trying to understand how someone:

breaks down an ambiguous problem
collaborates with AI tools
validates correctness
makes trade-offs under time pressure
communicates their reasoning

In other words, the process has become more important than the product.

What High-Performing Hiring Processes Look Like Now

Live, progressive build sessions reveal real capability

One of the most effective modern interview formats is a short live session that begins with a deceptively simple task and gradually introduces real-world complexity.

At first, the problem is trivial. A strong candidate can solve it in one prompt.

But then new constraints appear:

performance requirements
data consistency issues
integration challenges
evolving product needs

This forces candidates to move beyond generation into engineering.

In this environment, you are not judging whether they “get to the final answer.” You are watching how they:

decide what to build first
use AI to accelerate without losing control
recover when something breaks
explain their own code

That is exactly what the job requires.

AI-integrated architecture interviews test real job readiness

Traditional system design interviews often test theoretical knowledge. Modern teams are replacing them with practical discussions that center on building features that actually use LLMs.

Instead of asking someone to “design a scalable chat app,” leading companies are asking:

“How would you design a document processing workflow that uses an LLM to extract structured data?”

This immediately reveals whether a candidate understands:

how LLMs behave in production
how to manage latency and cost
when to use structured outputs
how to evaluate reliability
how to design fallbacks

It also shows how they handle feedback. In real engineering environments, ideas are challenged constantly. The ability to defend, adapt, and refine a plan is far more valuable than reciting patterns.

AI interaction transcripts show how engineers actually think

One of the most interesting new evaluation tools is asking candidates to submit their AI session history along with their code.

This shifts the focus from:

“What did you build?”
to
“How did you build it?”

When you read a transcript, you can see:

whether they decompose problems into logical steps
how specific and intentional their prompts are
how quickly they detect incorrect output
whether they blindly accept or actively shape results

Two repositories can look identical.
Two thought processes rarely are.

This has become one of the highest-signal evaluation methods in AI-native teams.

Real work trials still work, but the success metrics have changed

Paid work trials remain the most reliable predictor of success because they simulate the real environment: your codebase, your communication style, your product constraints.

However, what you measure during that trial is different now.

You are not counting lines of code. You are observing:

how quickly someone produces production-quality pull requests
whether they follow your existing patterns without being told
the quality of the questions they ask
their ability to operate autonomously in an async team
how clearly they communicate progress and blockers

This is particularly important for distributed teams, where delivery speed and clarity matter more than interview performance.

The Skills That Matter Most in AI-Native Engineers

Fundamentals still determine who actually benefits from AI

There is a misconception that AI reduces the need for strong engineering foundations.

In reality, it magnifies the difference.

Strong engineers use AI to move faster because they know what “correct” looks like. They can detect subtle bugs, challenge inefficient solutions, and refactor generated code into something production-ready.

Weak engineers become dependent on AI without understanding what it produces. They generate more code, but deliver less value.

The simplest way to test this is to ask a candidate to walk through their own implementation line by line. If they truly understand it, their explanations will be precise and confident. If they do not, the gaps appear immediately.

Tooling fluency is the new productivity multiplier

Great engineers have always cared deeply about their tools. That has not changed. What has changed is how visible this is.

You can now observe:

how they structure prompts
how they iterate on outputs
how they combine multiple tools
how they validate results

The best candidates are intentional. They do not treat AI as magic. They treat it as a system they control.

This translates directly into day-to-day productivity.

Builder energy is the fastest screening filter

In a 30-minute conversation, one question eliminates the majority of candidates:

“What have you built recently using AI in a real environment?”

People who are excited about their craft will have an immediate, detailed answer. They will talk about trade-offs, failures, iterations, and learnings.

People who are not will speak in generalities.

In a market where resumes are increasingly similar, genuine builder behavior is one of the strongest differentiators.

Why You Should Not Ban AI in Interviews

Some organizations respond to this shift by trying to remove AI from the interview process.

This is a mistake.

That approach evaluates a world that no longer exists.

Your engineers will use AI every day on the job. The goal of the interview is not to test whether they can work without it. The goal is to test whether they can use it intelligently.

The future belongs to engineers who produce better outcomes because of AI, not in spite of it.

What This Means for Global Hiring and LATAM Teams

As AI reduces the importance of manual coding speed, the global talent pool becomes dramatically more competitive.

Time zone alignment, communication skills, ownership mentality, and delivery consistency now matter more than ever.

This is one of the reasons companies hiring in Latin America are seeing outsized results.

Engineers in the region are often:

deeply experienced in remote collaboration
comfortable working in async environments
focused on shipping real product rather than optimizing for interview performance

When your hiring process evaluates thinking, execution, and real-world delivery, these strengths become obvious.

A Modern AI-Native Hiring Framework

A hiring process that consistently produces high-quality outcomes typically includes:

A short builder screen that looks for real projects and depth of explanation.
A system design discussion centered on an actual LLM-powered feature.
A live build session where AI is allowed and the workflow is observed.
A paid work trial that measures real delivery inside your environment.

This structure aligns the interview with the job itself, which is the most reliable way to make strong hiring decisions.

Your Hiring Process Is Now Your Competitive Advantage

Every company has access to the same models.

Every engineer has access to the same tools.

The differentiator is no longer the technology.

It is your ability to identify and attract the people who use that technology best.

Organizations that redesign their hiring around thinking, tool fluency, and real delivery will consistently hire from the top tier of global talent.

Those that continue to evaluate for a pre-AI world will struggle, no matter how strong their brand is.

How Mismo Helps Companies Hire AI-Ready Engineers

At Mismo, we help companies hire engineers in Latin America who are already operating in this new reality.

They are not just strong coders. They are:

fluent in modern AI workflows
experienced in real-time collaboration with US teams
focused on shipping production outcomes

If you are rethinking your hiring strategy for the LLM era, we can help you design a process that identifies the right talent and integrates them quickly into your team.

Careers Blog, Engineering, Find Remote Work

Technical Autonomy Is Not Freedom: It’s Structured Responsibility

Most engineers have, at some point, heard the promise of “total autonomy”—that appealing idea of making decisions without friction, bureaucracy, or endless approval layers, as if technical freedom were the ultimate destination of every software engineering career.

In remote and distributed teams, especially within the software development ecosystem in Latin America, that promise often blends with professional pride, access to global projects, and the feeling that world-class technology is being built from LATAM.

Yet over time, a question emerges that many developers rarely voice out loud: is what we call autonomy truly technical empowerment, or is it simply being left alone to make critical decisions without context, without support, and without a clear structure to sustain their impact?

Software Development as Professional Identity, Not Just Execution

Software engineering has never been just about writing code that works. It is about taking responsibility for decisions that affect real users, business models, entire teams, and the long-term evolution of systems.

Every architectural choice, every library selected, and every technical trade-off accepted carries consequences that extend far beyond a single sprint or release.

That is why autonomy, when offered without shared criteria, without a clear technical vision, and without accessible leadership, stops being a growth opportunity and quietly becomes a risk—for both the product and the engineer.

Pride in being a developer does not come solely from technical mastery, but from understanding the impact of what we build and knowing that our decisions align with a broader purpose.

In that sense, autonomy without structure does not strengthen professional identity—it erodes it, by forcing individuals to carry alone responsibilities that should be collective.

LATAM Talent, Global Impact, and the Real Weight of Decision-Making

LATAM talent has become a cornerstone of nearshore software development, not only because of technical skill, but due to resilience, cultural adaptability, and a strong capacity for continuous learning.

Engineers from Colombia, Costa Rica, Mexico, Brazil, Argentina, and across the region now lead critical systems for global companies, directly impacting millions of users and high-stakes business decisions.

This growth has elevated the role of the Latin American developer—but it has also increased the complexity of the decisions expected from them.

The greater the global impact, the greater the need for clear technical structures. Not every decision should rest on a single individual, no matter how senior they are.

This is where many organizations confuse autonomy with abandonment—delegating decisions without providing context, without defining standards, and without creating real spaces for technical discussion.

For experienced engineers, demanding autonomy also means demanding clarity: living roadmaps, shared architectural principles, and technical leadership that stays present instead of disappearing.

Community, Structure, and Responsible Autonomy in Remote Teams

Real autonomy exists when engineers can decide with complete information, visible technical agreements, and the confidence that they are not isolated in their decisions.

Organizational abandonment shows up when there are no review spaces, when decisions go undocumented, and when failures are only discovered in production—too late.

In remote teams, this distinction becomes even more critical, because distance amplifies both healthy culture and unhealthy practices.

That is why developer community is not a romantic ideal—it is a technical necessity to sustain quality and learning.

Practices like deep code reviews, intentional pair programming, and active mentorship turn individual decisions into shared knowledge.

In a healthy engineering culture, autonomy is not measured by how many decisions you make alone, but by how many you can sustain, explain, and evolve alongside other engineers.

Structure does not limit creativity; it protects it—by enabling experimentation without compromising system stability or team health.

Mismo: Supported Autonomy, Purpose-Driven Engineering

At Mismo, autonomy is understood as a responsible practice—one where engineers have room to decide, but are never left alone with critical decisions.

The culture encourages real collaboration across countries, human-centered technical leadership, and environments where asking questions is a sign of professional maturity, not weakness.

Distributed teams do not operate as silos, but as knowledge networks strengthened through communication, continuous learning, and trust.

This approach allows LATAM talent to create global impact without sacrificing identity, growth, or technical quality.

More than executing tasks, engineers participate in the evolution of products, architectures, and sustainable ways of working.

Here, autonomy is not sold as absolute freedom, but as shared responsibility—supported by living processes and present people.

Building the Future with Conscious Autonomy

The real challenge for modern engineering is not choosing between autonomy and control, but designing cultures where responsibility is distributed and visible.

As developers in Latin America, we have a historic opportunity to prove that our talent does more than execute—it leads with judgment, technical ethics, and a strong sense of community.

Mature autonomy is not the absence of structure; it is a commitment to decisions that endure over time.

We are a generation of LATAM engineers building the future—not through improvisation, but through conscious autonomy, real collaboration, and the pride of creating technology with purpose.