Technical interviews for data analyst roles are rarely about tricky brainteasers. Most of the time, they focus on fundamentals.
The problem is not whether you’ve “seen” a concept before. The problem is whether you truly understand it, can explain it clearly, and can connect it to real business use cases.
In this article, I’ll walk you through the most common technical interview questions for junior data analyst roles. I’ll cover:
- Excel
- SQL
- Power BI
- Tableau
- Python
These are not senior-level edge cases. These are core concepts. If you apply for a data analyst role, you should be comfortable answering them without hesitation.
Excel – Still Very Much Alive
Even if a company primarily works with SQL or BI tools, Excel never fully disappears. At some point, someone from the business side will send you a spreadsheet.
And then you’re responsible for making sense of it.
What Are the Limitations of VLOOKUP?
This question appears surprisingly often.
VLOOKUP is everywhere. In legacy files. In trackers that have been edited by five different people over the years. In reports that “somehow still work.”
The key limitations:
- It only looks to the right
The lookup column must be the leftmost column in the table. You cannot look left without restructuring the data. - Sensitive to structural changes
If someone inserts a column in the middle of the table, your column index number might now point to the wrong column. - Performance issues with large datasets
When you work with hundreds of thousands of rows, Excel slows down. VLOOKUP is not designed for heavy data processing.
If you mention alternatives like XLOOKUP or INDEX + MATCH, you show that you understand both legacy and modern solutions.
What Does the #N/A Error Mean and How Do You Handle It?
#N/A simply means: no match was found.
It is not a broken formula. It is a signal that the lookup value does not exist in the reference table.
How do you handle it?
- Data validation to reduce user input errors.
- IFERROR or IFNA to replace the error with something meaningful like “Not found” or 0.
Good analysts do not just “hide errors.” They understand what the error communicates.
SQL – The Core Skill
If the company works with databases, SQL is non-negotiable.
And the questions are usually very classic.
What Is the Difference Between LEFT JOIN and INNER JOIN?
This is one of the most common SQL questions.
- INNER JOIN returns only matching records from both tables.
- LEFT JOIN returns all records from the left table and matching records from the right table.
But here’s what really matters:
LEFT JOIN is often safer.
If you are not 100% sure that the relationship is one-to-one, INNER JOIN can unintentionally remove data. And removing data can lead to wrong business conclusions.
Interviewers want to see whether you think about consequences, not just syntax.
JOIN vs UNION
This question tests your understanding of data structure.
- JOIN adds columns by combining tables side by side.
- UNION adds rows by stacking tables on top of each other.
Important differences:
- JOIN increases the number of columns.
- UNION increases the number of rows.
- UNION requires identical structure: same number of columns and compatible data types.
JOIN is used more frequently in practice, but you need to understand both.
WHERE vs HAVING
Both filter data.
The difference is timing:
- WHERE filters raw data before aggregation.
- HAVING filters aggregated results after GROUP BY.
Example:
If I want to see segments with more than three customers, I use HAVING with COUNT().
WHERE, on the other hand, could filter specific customer IDs before grouping.
Understanding this difference shows that you understand query logic, not just keywords.
Power BI – It’s About the Data Model
Power BI interviews are rarely about colors or formatting. They focus on logic and data modeling.
Measure vs Calculated Column
This is a foundational question.
- A calculated column is computed during data refresh and stored in the model.
- A measure is calculated dynamically depending on filter context.
Measures are essential for KPIs and interactive dashboards because they respond to slicers and filters.
If you do not understand this difference, your reports will behave unpredictably.
What Is a Relationship and Why Does Direction Matter?
Relationships connect tables in the data model.
The direction of filtering determines how tables influence each other.
If you configure it incorrectly, you may:
- Hide valid records
- Duplicate values
- Draw incorrect conclusions
This question checks whether you understand that BI tools are not just visualization tools. They are logical data models.
Slicer vs Page or Report Filter
A slicer gives control to the end user.
Page-level or report-level filters operate in the background.
Good report design ensures that users understand what is filtered automatically and what they can filter themselves. Lack of clarity leads to confusion and misinterpretation.
Tableau – Deeper Conceptual Questions
If you apply to a Tableau-focused company, expect conceptual questions rather than simple definitions.
Extract vs Live Connection
Two types of connections:
Live connection:
- Queries the data source in real time
- Always up to date
- May slow down dashboards and increase costs
Extract:
- Stores a snapshot of the data
- Faster performance
- Requires scheduled refresh
It is a trade-off between freshness and performance.
What Is Level of Detail (LOD)?
LOD expressions allow you to calculate metrics independently from the current visualization’s level of aggregation.
Example:
You want to show the overall average delivery time across the entire dataset while displaying data at the supplier level.
Without LOD, your calculation depends on the current granularity. With LOD, you can define aggregation explicitly.
This question checks whether you understand aggregation mechanics.
Dual Axis – When and What to Watch Out For
Dual axis allows two measures to be displayed on the same chart with separate axes.
For example:
- Revenue
- Costs
The key rule: synchronize axes whenever appropriate.
If not synchronized, you risk misleading users visually. A line might appear steeper simply because the scale is different.
As analysts, we are responsible for clarity and accuracy.
Python – Cross-Functional Understanding
Python questions vary depending on the company.
Sometimes it’s required. Sometimes it’s optional.
List vs Dictionary
A list:
- Ordered collection
- Accessed by index
A dictionary:
- Key-value pairs
- Accessed by key
This basic question checks whether you have actually written Python code.
What Is a DataFrame?
A DataFrame is a tabular data structure in the Pandas library.
You can think of it as:
- A SQL table
- An Excel sheet
It is the foundation of data analysis in Python.
If someone cannot explain what a DataFrame is, they probably have not worked with real data in Python.
When to Use Python vs Excel vs SQL?
This is more of a business thinking question.
- SQL – when working directly with databases.
- Excel – quick ad hoc analysis.
- Python – automation, API integration, advanced analysis, scalability.
Python becomes useful when traditional reporting tools are not enough.
It’s Not Just About Knowing the Answer
Here’s something important.
Technical interviews are not only about correctness. They are about clarity, confidence, and reasoning.
If you need to look up every answer, you are not ready yet.
You should reach a point where these fundamentals are automatic. Where you can explain them calmly and clearly.
If you feel your knowledge is scattered, the solution is structured learning. That’s exactly why I built KajoDataSpace: to guide people from Excel to SQL, through BI tools and Python, in a logical order instead of random tutorials.
Final Thoughts
Technical interview questions for data analysts are not mysterious.
They revolve around:
- VLOOKUP limitations
- JOIN vs UNION
- WHERE vs HAVING
- Measure vs calculated column
- Extract vs Live
- List vs dictionary
These are fundamentals.
If you master them and can explain them clearly, you are in a strong position.
If you found this article helpful, share it with someone preparing for a data analyst interview. It might help them walk into that interview with more confidence and structure.
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
Did you like this article 🙂?
Share it on Social Media 📱
>>> You can share it on LinkedIn and show that you learn something new every day.
>>> You can throw it on Facebook – and perhaps help a friend of yours who is looking for this.
>>> And remember to bookmark this page, you never know if it won’t come handy in in the future.
You prefer to watch 📺 – no problem
>>> Subscribe and watch my English channel on YouTube.
Prefer to read in Polish? No problem.
Other interesting articles:
