Interview Query vs DataDriven for Data Engineer Interviews

Interview Query sells 9 learning paths with 350+ lessons between them. Data engineering is 1 path. Beside it sit paths for data science and data analytics, and for product metrics. Statistics and A/B testing share one path, and modeling and machine learning share another. The remaining paths teach SQL or Python, and probability is covered as well. Its pricing page counts 1,000+ interview questions, 6,000+ company guides and 50+ take-home challenges.

That mix shapes the questions. A typical Interview Query SQL question finds the 5 users with the longest streak of daily visits, or flags which purchases are a customer's first in a product category. The first is gaps and islands and the second is a first-or-repeat flag; both are real interview patterns that turn up in data engineering screens. What the library has less of is the pipeline side of SQL: deduplicating events that arrive twice, building a slowly changing dimension, writing a load that can rerun without double counting.

DataDriven covers only data engineering. Its {COUNT:total} problems are real data engineering interview questions, tagged with {COUNT:companies} employers. They span SQL and Python, with separate sets for PySpark and for the data modeling and pipeline design rounds. None of it is spent on statistics or probability, and none on product sense.

Interview Query's Python courses in the data engineering path split 2 ways. Data Structures and Python Questions: Hard review the algorithm and data structure questions a software interview asks. Data Pre-Processing is the course closest to the job: cleaning and reshaping data before it is used.

A data engineering Python round usually hands you raw records and asks you to make them reliable: parse a log file where some lines are malformed, keep the latest record per key when the same event arrives twice, flatten nested JSON into rows, or stream a file too large to load with a generator. The interviewer watches how you treat bad input and the edge cases a pipeline meets every night, and whether your code stays within memory, more than how fast you invert a tree.

DataDriven's {COUNT:python} Python problems are written in that shape, with plain Python first and libraries second. Interview Query's Data Pre-Processing course is good extra practice for the same skill; its algorithm courses matter only if your target company runs a software style coding screen.

Interview Query's data engineering path has no Spark course. Its 8 courses cover SQL and Python along with dimensional modeling and database system design, and its plans describe the coding questions as Python and SQL. If your target company runs on Databricks or a Spark cluster, you need another place to practise.

A Spark interview for a data engineer rarely stops at a DataFrame that returns the right rows. The follow-up questions are about how the job runs: why a join shuffles both sides and when a broadcast join avoids it, what a skewed key does to 1 task while the others sit idle, and how partitioning on write decides how much a later query reads. DataDriven runs PySpark in the browser across {COUNT:spark} problems, and its mock interviews ask those follow-up questions once the code runs.

Interview Query's Dimensional Modeling course teaches the framework for designing a warehouse around facts and dimensions, tied together by a grain. That is the right vocabulary, and reading it is a fair way to learn it.

The round itself asks for more than vocabulary. The interviewer names a business process such as rides or orders and you design the tables live. Sometimes the process is subscriptions. You declare the grain first and then the dimensions and facts, and you defend the design when they ask what happens to last year's revenue by city after a customer moves. Most mistakes happen at the grain: a fact table with a vague grain double counts the first time someone joins it to a second fact.

DataDriven's {COUNT:data_modeling} data modeling problems are answered on a schema canvas, where you place tables with their columns and primary and foreign keys, then draw the relationships between them. Each answer is checked for its grain and keys, and for how it keeps history.

Interview Query's Database System Design course poses questions such as an international e-commerce warehouse: "What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?" The answer is a written solution, and the comments under it add designs that name Kafka and Spark alongside Snowflake and BigQuery.

Reading a good architecture is useful, but the round asks you to produce one. A strong answer names each stage and why it is there: raw data lands unchanged so any day can be rebuilt, a transform builds the reporting tables, a quality gate stops a bad load before anyone sees it, and the interviewer then pushes on the edges, such as orders that arrive 2 days late or a backfill after a bug. Practising in prose alone tends to produce answers that list tools without saying what each one guarantees.

DataDriven's {COUNT:pipeline_architecture} pipeline design problems are answered on a canvas built from the vocabulary the round is held in. Sources and queues feed transforms and quality gates, and those write to storage that consumers read.

Interview Query offers 2 kinds. Its peer mock interviews match you with another member over live video, and each of you fills in a feedback form afterwards. Its AI Interviewer runs a live interview that assesses technical and communication skills, 3 a month on IQ Coder and 10 a month on IQ Premium. A peer mock is good practice at talking through a problem, though how useful the feedback is depends on who you are matched with.

DataDriven runs a mock interview for each of its {COUNT:total} problems. That covers every round of a data engineering loop, the 2 design rounds included. Each one ends with a hire or no hire verdict and the reasons for it, so you can see which part of your answer cost you.

Interview Query's 6,000+ company guides are its best feature for a data engineer. A guide describes how a company interviews for a role, down to the rounds and the kinds of questions asked, and reading one the week before an onsite tells you what to expect from that loop. Its 50+ take-home challenges are good rehearsal for companies that send a take-home instead of a live coding round.

DataDriven's problems are tagged with {COUNT:companies} employers and drawn from {COUNT:interviews} interview reports. On either site, treat company tags as a guide to a company's style and topics rather than a forecast of your exact question. Loops change, so you may get a problem you have not seen. Practise unseen problems of the same kind.

Interview Query is the better fit when the role is closer to analytics than to pipelines: an analytics engineer or product data role whose loop is SQL and product metrics plus a case study. Its statistics, A/B testing and product metrics paths cover rounds DataDriven does not offer at all.

It is also worth keeping for research. If you have onsites at 3 companies in the same month, its company guides tell you how each runs its loop, and its take-homes rehearse the format some teams use instead of live coding. The StrataScratch comparison for data engineers and the DataLemur comparison weigh the 2 other analytics-first sites the same way.

With 6 weeks before a data engineering loop, spend the first 2 on SQL in the dialect your target company runs, until a medium problem takes under 20 minutes. Window functions carry most of a screen, and the SQL window functions interview questions cover ranking and running totals the way interviewers ask them. The same questions include gaps and islands.

Spend weeks 3 and 4 on the rounds Interview Query's path gives least time to. Draw 2 or 3 designs a week for data modeling and pipeline design on DataDriven's canvases, and add Python problems that parse and deduplicate records or stream them, plus PySpark if the team runs Spark. Use the last 2 weeks for mock interviews of every round, and read Interview Query's guide for each company in the week before its onsite.