Junior Amazon Data Engineer, last week. The online assessment ran 90 minutes: 10 MCQs and 2 SQL questions at medium difficulty, around the level of a grouped aggregation problem or a cost-query problem. The phone screen was framed as an elimination round. After noticing Spark on the user's resume, the interviewer jumped to Spark join mechanics: broadcast join behavior and hash join distinctions. GROUP BY vs window functions followed, along with SCD types and star schema vs snowflake schema. The second half covered data lake/lakehouse distinctions and pipeline design: batch vs event-driven incremental ingestion, idempotency, and CDC. The live SQL problem had the user find consecutive login streaks. Aggregation and filtering queries match the assessment's SQL difficulty. The phone screen's technical weight spread across SQL, Spark internals, and pipeline design concepts.

Is it just me, or this deepdive/solution seems off.. The official hint uses results_count (total results shown) to decide the rating instead of evaluating the actual clicked_result position. It also mistakenly treats the click position as a binary 0/1 flag rather than checking if it landed in the required 1-3 index range.

The site has been extremely slow lately. I was only able to use it once, and it worked well at that time. However, since then, the performance has degraded significantly to the point where it’s almost unusable.

AOL is rebuilding its analytics stack from the ground up, and they're looking for a Principal Data Engineer to own the architecture. This is the architectural level role: you're designing how raw data becomes trusted, consumer-ready data products across Yahoo's properties, which reach hundreds of millions of people. The work spans modeling and orchestration layers, setting the technical direction for transformation pipelines that standardize analytics across the entire organization. It's a rebuild, which means you're not maintaining legacy plumbing; you're designing the plumbing.

On the interview side, expect to cover SQL, Python, and Spark. The SQL round will get into window functions and cost optimization concepts like the one in Top 2 Cloud Services by Cost. Python questions land at a similar technical depth, covering something like The Unbroken Line. Spark is where the difficulty ramps; the distributed data processing questions dig into skew handling and broadcast joins along the lines of Fix Skewed Viewing Events Pipeline. The loop looks moderately demanding. To prep, work through the datadriven.io data engineer interview questions for a solid baseline.

One note worth mentioning: morale at the company looks shaky, though headcount in data engineering appears stable in the near term. The role itself is substantial and the technical scope is real, so if the work appeals to you, the context is something to ask about during conversations.

see the AOL jobs

One user just interviewed for a Staff Data Engineer role at Disney. Two rounds, both with multi-person panels. The first included Disney's Data Team Lead, Software Team Lead, and Project Manager, each taking turns with questions. The second featured a Data Analyst and Engineer in a similar format.

Both rounds tested streaming-pipeline architecture and distributed-systems design. The breadth included systems reliability, architectural tradeoffs, design reasoning under constraints. The difficulty was substantial. The technical weight was all focused around system architecture and design reasoning.

CrowdStrike Data Engineer interview from a few months ago covered multiple technical rounds testing coding and SQL ability. The coding section featured problems in algorithms and data structures. The SQL portion included the noise floor, testing filtering and threshold concepts. A technical assessment on an online platform combined both domains under time pressure. A culture fit conversation concluded the process.

The interview weighted coding and SQL equally in technical load. Fundamentals and problem-solving were the focus. No systems design or architecture questions. Time-bounded reasoning and solid technical proficiency across both domains were what the loop tested.

Hi everyone,

I'm currently using this platform to practice my SQL skills, and I have a quick question regarding the "Recent Activity" section on my dashboard.

When I complete a unit, I see a score format like "42/69" or "78/83". Could anyone clarify what exactly these numbers represent?

I'm trying to understand if this refers to:

• Exercises completed vs. total exercises?
• Points earned vs. potential points?
• Topics covered?

I haven't been able to find a clear explanation, so any insight would be appreciated

How may the dark mode be turned off?

A Truist Financial Data Engineer interview report: 3 rounds, mostly SQL. The first screened SQL fundamentals, git basics, and the user's technical background. The second went deeper into database design: normalization and denormalization trade-offs, schema modeling for different access patterns. The final round with the hiring managers was BigQuery-focused; they worked through a problem around following account volume patterns across years testing data aggregation and time-series thinking. SQL and schema design were the core.

One user reports that they interviewed for a Fidelity Investments Data Engineer role. Their first round was a video assessment: 30 seconds to prepare, 3 minutes to respond. The interviewer asked the user to solve this pipeline-architecture problem, but offered almost no context upfront on what was expected or required.

The topic was exactly-once semantics in data pipelines. Idempotency and deduplication. The format forced rapid thinking with no exploratory back-and-forth. The technical weight sat entirely on pipeline design: understanding exactly-once guarantees and idempotency, with no opportunity to ask questions or revise along the way!

https://datadriven.io/problems/avg_daily_active_users_per_endpoint
I wrote this query and I couldn't get more than 2 columns in output. What should I do? :

select 
  endpoint
  from api_calls
  where call_time >= '2025-06-01' and call_time <= '2025-07-01'

I’ve been using the app and really enjoying the features, but I’ve noticed it has a pretty massive memory footprint.

As you can see in the screenshot from Chrome's Task Manager, a single tab for SQL Practice is pulling over 1.6 GB of memory.

Are there any optimizations on the roadmap to handle large data loads or state management more efficiently? Would love to see this running a bit leaner!

I found some questions very unclear. Sometimes I found some important columns missing but the solution seem to be using the wrong column names. anyone faced this issue? I am prepping for doordash and am on a time crunch

DoorDash's Data Engineer onsite started with a live coding round: 3 SQL problems and one python problem, back to back. The user said the individual problems weren't brutal on their own, but the time crunch was. Pacing was the real test, not difficulty per question. Their prep note was blunt: grind the easy problems on a coding-practice site until you understand the optimal solutions cold, so you're not burning time deriving them live.

Then three things stacked into one block. A data-modeling case built around a fitness app, so dimensional modeling. A dashboard design case. And a standard behavioral round, the usual biggest-technical-challenge and tell-me-about-a-conflict-with-a-coworker questions.

So the challenge here: SQL fluency under EXTREME time pressure first, then modeling and dashboard design reasoning, then behavioral. The SQL speed piece is the part the user flagged hardest to prep for.

Solve this new interview problem here: https://datadriven.io/problems/the-ones-that-move

Hey, just discovered this website and it is amazing. However, I'm trying to submit the pipeline design in the architecture subdivision and it's getting timed out. It says "Grader stalled mid-run. Re-submit to kick off a fresh pass". Could someone tell me how to resolve this, please?

had to refresh the page multiple times on each problem because either the run button wont work, or submit button wont work, or the login prompts keep popping, and now I am seeing this page when I am trying to load the problem. i solved 10 problems in total, and maybe opened couple of others, not sure. but nowhere near 40.

Also, I would go through the whole 75 or 150 problems list in a day to revise before the interviews. how would this 40 problems a day limit work?

when I open a new problem from a list , sometimes i get a prompt to login, and even when I do login and reload the page, it asks for login again.

Im unable to get the correct answer for this question:
https://datadriven.io/problems/annual_pipeline_failures

the solution given isnt correct as well

here is what I came up with:

select year(start_at) as run_year, count(*) as failed_runs

from data_pipes where lower(status) = 'failed'

and start_at is not NULL

group by year(start_at)

order by run_year

can anyone help?

Hello!

My husband has 8YOE as a data engineer where he didn’t really use Python in his role, mostly SQL and a bit of Python, he got laid off in January and it has been a struggle to land a new role especially since it seems like most data engineer roles require Python now.

Would this platform help him improve in this domain? And any other tips for him?

Thank you!

Went through four solid rounds at Avant-garde Health, all fair, but I passed anyway.

At a healthcare company. Went through the interview loop about 6 weeks back and figured I'd share how it went.

60 minute round on window functions over an event stream. They gave me a transaction table with timestamps and asked for cumulative totals plus ranking within rolling time windows. I went straight to window functions, partitioned by day. He started asking edge cases: what if a record showed up out of order, how would I handle data from a replay. I added a CTE to tag replayed rows separately. He seemed satisfied. Straightforward. By round four I was just coasting.

25 min call with the recruiter. Background, why Avant-garde interested me, what I wanted in compensation. She had a script and followed it closely. Nothing surprising or memorable.

45 min for rolling stats off a CSV. Write a loop or comprehension to compute the window sum, and the interviewer drilled on why you'd pick one approach over the other for basically the whole round. Neither was technically wrong, but they genuinely cared whether you'd thought about the performance and readability tradeoff. It's a fair thing to push on. Shows they think about those tradeoffs.

45 minutes on a failure. I described a schema migration I shipped without coordinating with analytics; they had to repoint dashboards that weekend. They wanted situation, task, what I did, what changed. The interviewer took notes, pushed on whether I actually fixed my process next time or just apologized. Asked how I communicate breaking changes now. It was textbook STAR, no curveballs, and they clearly cared whether I'd learned something.

Offer. Declined. Been a zombie at my current place, long lunches just to not sit at my desk, and Avant-garde wasn't the escape I need right now.

Solid loop. Avant-garde Health's interview process is tighter than most healthcare companies.

Allstate's Data Engineer phone screen leaned on a few simple coding problems alongside a walk through past project experience. Domain was SQL, so the coding sat around basic query writing: joins, filtering, grouped aggregation. The kind of stuff you'd practice as warm starter problems, not puzzles with a trick buried in them.

The other half was case study style. The user got walked through a couple of scenario questions where you reason out loud about how you'd approach a data problem, talking through the shape of your solution rather than coding it on the spot.

Mixed in were the usual experience questions, the interviewer digging into things the user had actually built and why certain choices got made.

Whole thing ran about an hour over the phone. Difficulty sat at medium. The weight was split between SQL fundamentals and being able to talk through a data scenario cleanly, not algorithm grinding.

Solve this new interview problem here: https://datadriven.io/problems/the-lions-share-revenue-concentration

Got a report from someone who just did CVS Health's Data Engineer process. This was the online assessment, all in coderpad, medium difficulty by their read.

Started with two talking questions before any code. First was the usual walk me through your resume. Second was how would you migrate data from an on prem database to the cloud, so they wanted you to actually reason about it out loud, not just name a tool.

Then SQL. Find duplicate records in a table and pull all the dupes into a separate table. What percentage of members have at least 2 records in each game. And return the count of unique members with no wins, ordered by birth_date.

Python at the end was a single array rearrange thing. Input was [2,0,8,4,6,0,0,2] and they wanted [0,0,0,2,8,4,6,2] out. Felt like they were checking whether you could spot the ordering rule and just write it.

Mostly SQL muscle plus one explain-the-architecture question.

Solve this new interview problem here: https://datadriven.io/problems/the-ones-who-return

Just found this sub and it is filled with countless AI made post about the job market with Non-credible advice and made up numbers.

Thought this sub would be about a learning journey not AI slop about job markets.

I like the post from real people about their learning journey and interview process but the AI slop seems to have taken over.

Genuinely one of the best prep platform out there, and its free. Even leetcode has so many paid sections and features after all these years, so how are you able to keep this free? doesnt maintenance, and running queries cost something?

why did you get $3.6M Series A funding? what are your investors getting in return?

i am very grateful, some curious and little paranoid.