If you spend any time on LinkedIn or Reddit as a junior data analyst, you’ll eventually see the same argument:

“Python is the future.”

Or:

“SQL is the only thing that actually gets you hired.”

As someone with about a year of experience working in data projects, I used to think I had to choose one.

Should I go deep on Python because AI is changing everything? Or should I spend all my time becoming amazing at SQL because every job description seems to ask for it? After working on real-world data projects at DataSync, I think the answer is more nuanced.

The truth is: most junior analysts are asking the wrong question.

It’s not Python vs SQL.

It’s: What skills actually help you solve problems and become useful on a real data project?

First: Stop Thinking About Tools Like They’re Pokémon Cards

Early in my career, I thought learning data skills meant collecting technologies.

SQL? Tick. Python? Tick. Power BI? Tick. Tableau? Tick. Cloud? Tick.

But the analysts who become genuinely valuable are usually not the people who know the longest list of tools.

They are the people who can:

• Understand messy business problems
• Work out what data is needed
• Spot issues in data quality
• Communicate clearly with engineers and stakeholders
• Get to an answer without getting stuck

The tool matters.

But how you think matters more.

That said, tools still matter — especially when you’re early in your career.

So let’s talk about where to prioritise your time.

Why SQL Still Matters More Than Most People Think

If I had to give honest advice to a junior analyst in 2026, it would be this:

Become very good at SQL first.

Not because Python isn’t useful.

Because SQL shows up everywhere.

On most data projects, you are constantly:

• Pulling data
• Understanding schemas
• Joining systems together
• Validating outputs
• Investigating problems
• Checking data quality
• Exploring anomalies

And most of that work happens in SQL.

A surprising number of analysts try to jump straight into machine learning or advanced Python projects without being able to confidently write joins, window functions, or debugging queries. That creates a problem. Because on a real project, the person who can confidently answer:

“Why don’t these numbers reconcile?”

usually becomes more valuable than the person building a complicated notebook nobody understands.

What “good at SQL” actually means

You do not need to become a database engineer.

But I think junior analysts should become comfortable with:

• Joins (and when they break things)
• Aggregations
• Window functions
• Common Table Expressions
• Data validation checks
• Debugging logic problems
• Reading unfamiliar schemas
• Writing queries clearly enough for other people to understand

This alone will make you much more employable.

But Ignoring Python Is Also a Mistake

Here’s the thing. The world has changed. AI coding assistants have dramatically reduced the barrier to entry for Python. You no longer need to memorise every syntax pattern.

But that does not mean Python is irrelevant.

In fact, Python is becoming more valuable for analysts who want to move beyond basic reporting.

Python becomes incredibly useful when you need to:

• Clean messy files
• Automate repetitive work
• Process large datasets
• Analyse text or documents
• Work with APIs
• Build reusable workflows
• Do more advanced analysis
• Support data engineering teams

At DataSync, we increasingly see analysts who can bridge the gap between business understanding and technical execution becoming more valuable.

That often means SQL for understanding the data and Python for scaling the work.

The Real Problem: Most Analysts Learn in the Wrong Order

Here’s what I wish somebody told me earlier.

Many junior analysts try to learn Python before they understand data.

That’s backwards.

If you can’t:

• Explain what good data quality looks like
• Understand why a metric changed
• Trace lineage through systems
• Spot broken assumptions
• Ask good business questions

then more Python will not magically make you better.

You’ll just automate confusion faster.

A better progression looks something like this:

Stage 1: Learn SQL and data fundamentals

Understand data structures, joins, business logic, and how messy real-world data behaves.

Stage 2: Learn how projects actually work

Understand requirements, stakeholders, source systems, reconciliation, and documentation.

Stage 3: Add Python

Start automating repetitive tasks, cleaning datasets, and building repeatable workflows.

Stage 4: Combine both

Use SQL and Python together to solve bigger problems faster.

What Should You Prioritise in 2026?

If you’re early in your career, this would be my recommendation:

Prioritise SQL if:

• You struggle with data modelling concepts
• You cannot confidently debug data issues
• You still find joins confusing
• You are applying for junior analyst roles
• You want to become useful quickly on projects

Prioritise Python if:

• You already feel comfortable in SQL
• You repeat manual tasks every week
• You want to automate workflows
• You want to move toward analytics engineering or data engineering
• You want to stand out from other analysts

Best outcome?

Learn both.

But not equally, and not at the same time.

For most junior analysts:

SQL first. Python second. Problem-solving always.

One Last Thought

I used to think the best analysts were the people who knew the most code.

After a year working on real projects, I don’t think that anymore.

The best analysts are usually the people who can take a messy problem, ask smart questions, and help a team move forward.

SQL and Python are just tools.

Knowing when and why to use them is what actually makes you valuable.