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I'd love to help you prep for this role! Landing a 6-month Data Analyst internship at Lesaffre's international Baking Center is a fantastic opportunity, especially since it sits right at the intersection of marketing, digital tools, and their core bread-making expertise.

Before we dive into the specific tools they use—like Power BI, SQL, and their internal test-tracking app Bamareto—I'd love to get a better sense of your background and timeline so we can tailor this prep perfectly for you.

When is your interview scheduled for, and what is your current comfort level with tools like SQL, Power BI, or data modeling?

It's next week! I know some SQL and Power BI, but I'm still a beginner.

It's completely normal to feel like a beginner, but with your interview next week, we can easily turn that into a strength. I've designed a specialized preparation path to get you ready.

Since you have some basic knowledge of SQL and Power BI, we should decide on the best strategy for our limited time. We can either start by building rich talking points around how operates its global Baking Center network so you can ace the company-fit questions, or jump directly into the beginner-friendly tech concepts you'll face in the technical part of the interview.

Jump straight into beginner SQL & Power BI concepts

Let's dive straight into the technical side, starting with the SQL basics you'll need for this interview. Because Lesaffre's Baking Center uses transactional databases to track bread-making tests—like baking times, yeast types, and sensory scores—knowing how to pull and filter this data is crucial.

Retrieving and Filtering Baking Data

When you query a database, your primary tool is the to choose your columns, and the WHERE clause to filter your rows. For instance, if you are looking at a table of bread-making tests from the Bamareto app, you might want to find only the tests that used a specific yeast strain or exceeded a certain baking temperature.
SELECT 
    test_id,
    flour_type,
    baking_temperature_celsius,
    sensory_score
FROM 
    bamareto_baking_tests
WHERE 
    yeast_strain = 'Saf-Instant'
    AND baking_temperature_celsius > 200;

This query goes into the bamareto_baking_tests table, looks for any rows where the yeast strain is exactly 'Saf-Instant' and the temperature was set above 200 degrees, and returns just those four columns for analysis.

Let's test this concept out. Imagine the interviewer asks you to write a query to find all tests where the sensory_score is less than 5. How would you write that WHERE clause?

WHERE sensory_score < 5

Data Querying
LV 1
SQL
LV 1
Skills

Spot on. Your WHERE clause is exactly how you tell the database to filter out the noise and only give you the low-scoring tests.

Summarizing Baking Tests with Grouping

In a real-world scenario at Lesaffre, you won't just look at individual tests. Managers will ask you high-level questions like, "What is the average sensory score for each flour type we test?" or "How many total tests did we run last month?"

To answer these, we use like COUNT(), SUM(), and AVG(), combined with a GROUP BY clause. The GROUP BY clause acts like a sorting tray, gathering identical values in a column together so we can run calculations on each group.

SELECT 
    flour_type,
    COUNT(test_id) AS total_tests,
    AVG(sensory_score) AS average_score
FROM 
    bamareto_baking_tests
GROUP BY 
    flour_type;

This query groups all our baking tests by their unique flour types, counts how many tests were run for each, and calculates their average sensory score. It's a quick way to see which ingredients are performing best in the Baking Center.

How do you feel about this grouping logic? Would you like to practice writing one yourself, or should we move on to how we join different tables together?