Why ‘Becoming the Best’ Isn’t What Will Land You a Data Job (and What Will)

Stop Trying to Be the “Best” (It’s Not What Employers Care About)

You’re gearing up for your first data job. You’ve taken the courses, built some skills, and maybe dabbled in your portfolio—but every time you scroll through job ads or LinkedIn, a voice pops into your head:

“Am I good enough? How can I stand out? Someone out there is better, more skilled, more technical. Why would they hire me?”

Here’s the truth: you don’t need to be “the best” to land your first data job.

The idea that you have to somehow outrank, outshine, or out-skill hundreds of other candidates is not only overwhelming—it’s misleading. Employers aren’t scanning resumes for the “best data analyst on paper.” They’re looking for someone who brings value, solves problems, fits the team, and has the potential to grow.

That’s it.

Focusing on becoming “the best” will drain your energy, fuel impostor syndrome, and have you chasing a moving finish line. Instead, let me show you how to focus on becoming the kind of candidate who actually gets hired.

The Problem with Trying to Be “The Best”

Aspiring data analysts often fall into this trap of trying to be perfect:

• You study every Python library imaginable, convinced that knowing more will make you stand out.
• You delay applying for roles because you’re “still working on your portfolio.”
• You compare yourself to others and think, “They’re so much better; I’ll never catch up.”

But this mindset doesn’t help you—it paralyses you.

The truth is, there will always be someone “more experienced” than you. But employers aren’t hiring based on a perceived leader-board. They’re hiring based on:

1. Whether you bring real-world value to their specific needs.
2. Whether you’re adaptable, curious, and eager to learn.
3. Whether you can improve from wherever you are right now.

Stop stressing about outperforming everyone else. Start focusing on showing employers why you’re the right fit for their problems.

What Employers Are Actually Looking For

Forget the idea that employers want "the best candidate" in a general sense—they don’t. What they really want is someone who checks these three boxes:

1. You Understand Their Goals Can you help them solve business problems with data? Employers are searching for analysts who align with their needs, not those who know the most obscure functions in Python.
2. You Have the Mindset to Grow Your ability to learn and improve is far more important than being perfect on day one. A growth-oriented candidate who can adapt and learn on the job is worth far more than someone who ticks every technical box but is rigid and not collaborative.
3. You Problem Solve, Not Just “Analyse” A great data analyst doesn’t just crunch numbers. They connect dots that others miss, turning insights into impactful recommendations that matter to the organisation.

Let’s dig into how you can showcase these qualities without breaking yourself trying to be “the best”...

How to Become Valuable (Without Being “The Best”)

Here’s how to shift the focus and show employers why you’re exactly the kind of analyst they need:

1. Learn Enough to Start—Don’t Master Everything

You don’t need to master Python, SQL, Tableau, Excel, R, and Power BI before you apply for jobs. Instead, learn just enough to take action:

• Learn Python or SQL for basic data cleaning and analysis tasks.
• Practice a few starter projects to show understanding, even if they’re simple.

Employers know that you’ll gain additional technical knowledge on the job. What they care about is:

• Can you work with data at a functional level?
• Can you explain your process clearly?

Focus on depth over breadth. Mastery comes later—start with competence.

2. Build Portfolio Projects That Solve Real Problems

Your portfolio doesn’t need to be a showcase of technical brilliance. What it needs is to show how you think through challenges and deliver value.

Here’s how to structure your projects:

• Set a Real-World Theme: Choose projects that mimic business problems. Example: “How can we improve customer retention for an e-commerce business?”
• Focus on Simplicity and Relevance: Don’t just throw libraries and charts at a dataset. Show clear takeaways: “I used historical data to identify three trends driving churn and recommended targeted retention campaigns.”
• Iterate and Improve: Your first project won’t be perfect—that’s fine! Start small, refine it, and add new insights over time to show progress.

Remember: a portfolio that tells a clear story will always stand out more than one that's just a “technical showcase.”

3. Prioritise Communication and Collaboration

You know what candidates often overlook? The soft skills that immediately set you apart, like:

• Explaining complex analyses to non-technical stakeholders.
• Working with teams to translate data into action.
• Asking the right questions.

For example, imagine an employer needs help increasing sales. Two candidates present solutions:

• Candidate A dives deep into forecasting models but skips over explaining actionable insights.
• Candidate B clearly explains a trend in sales across regions and recommends actionable steps to increase conversions.

Guess who gets the job? Candidate B—because they connected the analysis to the employer’s actual goals.

Tip: During interviews, emphasise examples of collaboration, adaptability, and how your insights drove decisions.

4. Focus On Fit, Not Perfection

Ultimately, landing a data job isn’t about being “the best” on paper. It’s about showing employers that you’ll:

• Solve problems that matter to their team.
• Fit the culture and collaborate effectively.
• Grow alongside their organisation.

Instead of trying to be perfect, ask yourself:

• Do I understand this company’s challenges?
• Is my application tailored to show how I align with their needs?
• Am I showing my value—not just my knowledge?

Real-Life Examples: What Value Looks Like

Here’s how to stand out—even if you’re not “the best”:

Example #1: A Tailored Portfolio

Instead of a generic SQL project, imagine showcasing this:

• “I analysed user engagement data to identify a seasonal drop in platform activity and recommended timing marketing campaigns during traffic spikes. Insights improved campaign ROI by 15%.”

Done. Value is demonstrated.

Example #2: Collaboration in Action

During an interview, explain: “In my last role, I presented data insights visually to stakeholders who weren’t technical. By focusing on clear language and actionable summaries, our team streamlined budget decisions for the following quarter.”

No fancy machine learning needed—impact speaks louder.

Final Thoughts: Forget Perfection, Build Value

The idea that you need to be “better than everyone else” to land a data job is toxic nonsense. Employers don’t care if you’re the smartest candidate—they care if you’re the right fit.

What gets you hired isn’t mastery—it’s mindset.

Think of it this way: your first role isn’t your ultimate destination. It’s where you start building momentum. So focus on solving problems, communicating clearly, and showing how you think.

Be valuable. Be curious. Be adaptable.

The best will follow.