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Introduction to Technical Questions

"If I don’t write Python, I won't land a data analytics position at a big tech company."

"The only way to get promoted or hired as a senior analyst is to use machine learning and build complex models."

"I don't have a data background—it’s impossible for me to break into analytics at a top tech company."

"To get hired as a senior analyst, I basically need to become a data scientist."

There’s a lot of noise out there about what skills are actually required to land a data analytics role in big tech. We’ve cut through the noise and gone straight to the source.

We analyzed hundreds of job descriptions and spoke directly with data professionals at companies like Meta, Amazon, Google, and Uber. What we learned is clear:

Core technical skills

Let’s set the record straight.

Yes, some roles do require Python—but if your job description doesn’t, then mastering ML pipelines or building neural networks is not what’s going to get you the job.

Here’s what consistently shows up across most analytics roles:

Data Analyst 2.1.1 Essential Skills

1. SQL: non-negotiable

This is the one skill that shows up in almost every data analytics job at big tech firms—from entry-level to senior roles.

For example, you should be comfortable with the following:

  • Joins, subqueries, and CTEs
  • Window functions and aggregations
  • Writing clean, optimized, and readable queries

2. Excel & Google Sheets: still used, still tested

You may be surprised, even in big tech, spreadsheet skills still matter, especially for quick analysis and visualization, stakeholder requests, and dashboarding.

In data analytics interviews at top tech companies, Excel or Google Sheets may not appear as frequently as SQL—but that doesn’t mean you can ignore them. These tools still show up in interviews, especially in live case questions or take-home case studies, where you're expected to analyze datasets quickly and effectively.

Being fluent in Excel/Google Sheets demonstrates that you can move fast, build scrappy solutions, and extract insights without needing a full data stack, just like analysts often do on the job.

3. Data visualization & dashboarding: communicate clearly

Data storytelling isn’t about fancy charts or tools. It’s about clearly communicating insights and building dashboards that drive action.

4. Data analysis process

The best analysts don’t just jump into a dataset—they think critically about how and why they’re analyzing it.

Interviewers want to know:

  • How do you collect data, especially if no perfect dataset exists?
  • How do you clean and prepare messy real-world data?
  • How do you structure your approach to generate meaningful insights?

5. Statistical analysis: know the core, go deep if needed

No, you don’t need to be a statistician. But if you’re interviewing for roles that support product teams, experimentation, or marketing analysis, you’ll be expected to understand:

  • Probability and distributions
  • Hypothesis testing
  • Regression
  • Confidence intervals
  • A/B testing concepts

6. Python & R: only if the role requires it

Some analytics roles—for example, especially those with data science or engineering overlap—expect fluency in Python or R. Most do not.

One more reminder: always go back to the job description

If SQL and Excel are listed, focus there. If Python or advanced stats are required, dive deeper there.

And remember: knowing something isn't enough. You need to explain it, apply it, and communicate it clearly, especially under pressure.

This course is specifically designed to help you with that.

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