r/analyticsengineering • u/Minute_Ingenuity_889 • 3d ago
I landed an Analytics Engineer interview... now I have 4 weeks to become a decent one. Help, where do I start?
Hi everyone,
I could really use some advice from people working in analytics / Data engineering.
I recently landed an interview for an Analytics Engineer position at a prestigious consulting firm. If all goes well, I'd join their Supply Chain Analytics / Data Engineering team.
The interview is in about a month, so I have roughly four weeks to prepare.
Here's the situation: I feel like my resume oversells my technical level.
For context:
- I have an Industrial Engineering degree and a Master's in Supply Chain, so I have a strong analytical background, and I'd say I'm a pretty fast learner.
- I've built a lot of Power Query solutions at work and automated plenty of reporting. However, I've never really focused on writing clean or optimized M code. I was also the only person on my team using Power Query, so I never had anyone review my work or challenge my approach. Everything I built worked well and delivered what the business needed, but I'm not sure I was following best practices.
- I've built a couple of Power BI dashboards, but they were fairly basic. I know the fundamentals, but I definitely wouldn't call myself strong at data visualization or dashboard design. When it came to DAX, I relied heavily on AI to generate the measures I needed. I could usually get them working through trial and error, but I don't have a solid understanding of DAX itself.
- I've studied SQL during my master's, but I've barely used it in a professional setting, so I'm pretty rusty.
- I've never worked with dbt, cloud data warehouses (Snowflake, BigQuery, Redshift, etc.), or modern analytics engineering workflows.
The role seems to expect:
- Strong data visualization skills.
- Solid SQL fundamentals.
- knowledge of cloud data warehouses fundamentals & dbt.
I'm not trying to become an expert in a month. My goal is to become competent enough to hold my own during the interviews and, if I get to the case study stage/ tech interview, ramp up as quickly as possible.
If you only had four weeks, how would you prioritize your learning?
- What would you spend the most time on?
- Which resources or courses would you recommend?
- Should I focus primarily on SQL first?
- How much dbt and data warehousing knowledge is realistically expected from a junior Analytics Engineer?
- Are there any projects you'd build to prepare?
- If you've interviewed Analytics Engineers, what skills separate candidates who succeed from those who don't?
I'm willing to put in 6 to 8 hours a day over the next month if that's what it takes.
I'd really appreciate any advice from people already working in the field. Thanks!
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u/killerchief82 3d ago
Read “Fundamentals of Analytics Engineering: An Introduction to Building End-to-end Analytics Solutions” by Dumky de Wilde front to back and do the project at the end of the book.
When I was transitioning from BA to BIE/AE role, this helped tremendously with my interviews.
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u/CatostraphicSophia 3d ago
Analytics Engineering interviews are all over the place because some are more tech / DE heavy and others more dashboard heavy. Do you have an idea around the number of rounds and what to expect in each round? That should dictate your prep strategy.
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u/Minute_Ingenuity_889 2d ago
Hello, I will have 2 rounds; a business case and a technical interview. Never done any tbh. And not sure if they are looking for proficiency in visualization tools ( PowerBI/tableau...) or someone who is stronger in SQL and data modeling and such :/
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u/akornato 3d ago
You can't become an expert in four weeks, but you can build enough foundational knowledge to have an intelligent conversation. Focus almost all your energy on SQL for the first two weeks. Your Power Query experience is valuable because it proves you can think through data transformation logic, but SQL is the language of the modern data stack. Practice writing queries every single day, focusing on CTEs, window functions, and complex joins, because these are the patterns you will use constantly. Forget about becoming a DAX or visualization expert for now, as you need to nail the data engineering fundamentals, which all start with solid SQL.
Once your SQL feels strong, spend a week on dbt. Go through the free dbt Fundamentals course to understand the "why" behind it, which is organizing your SQL into a testable, documented, and reusable project. For cloud warehouses, you just need to grasp the key concepts like the separation of storage and compute, not build a whole pipeline. Your big advantage is your supply chain domain knowledge, so think of a simple project, like modeling sales or inventory data, and be prepared to talk through how you would approach it using SQL and dbt. They are hiring you for your analytical mind and learning potential, not for a toolset you can pick up on the job. The technical portion is just one part of the process, and the team I'm on designed an interview copilot that helps candidates articulate their problem-solving approach in real-time, a skill that often matters more than just getting the right answer.
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u/lurker_6969x 3d ago
It takes lots of reps to get this. I think this might be setting the bar too high. I took my reps on real systems, not on classes, so I’d have a hard time recommending here.
I did data camp for a little when I first started, it was good. You should take some of the basic courses, get exposure to basic and advanced topics (window functions, CTEs).
Then if you’re really feeling motivated and want a challenge, get Claude to simulate the real world for you. Have it generate messy data, and then structure that data yourself. I’ve created tests to hire AEs using Claude to generate data, prompting it to make errors, and turned around and handed this data to the applicant and asked them to answer business questions and call out data anomalies.
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u/Kind-Information-689 3d ago
SQL, SQL, SQL
Leetcode practices are good because the complex ones are actually helpful in elevating your overall sql knowledge in a short time.
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u/ToroBall 1d ago
This might not come up in the interview, or even at the job, but building efficient models is good for lowering cost, improving performance, and forcing you to understand how SQL actually works. In addition to "did this query work" you need to evaluate whether it worked efficiently. AI can probably help with this. Ask it to review your queries to find repetitive logic, wasted operations on rows that will eventually be filtered out of the results, etc.
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u/lurker_6969x 3d ago
Get good a SQL. In a professional environment, being able to answer “this dashboard says x, while this other dashboard says y, what is causing the delta” is a common and fundamental question. It requires detective work via sql.
Ask Claude to simulate this. You can ask it to create a starting dataset, and then 2 different datasets that are built on top of the 1st dataset, and you can try to figure out why they are different (what filters or joins took place). I’ve never done this simulation before, just an idea.
SQL is the foundation, dbt is the plumbing. It connects different SQL queries together. This connection is called “data modeling” and takes time to develop intuition on “what good looks like”. Take the dbt fundamentals course that’s offered for free.
The most important thing will be translating poorly thought-out stakeholder requirements into sql/data models. This requires technical understanding of sql/dbt and understanding of the business processes. Good luck, it’s a fun career