LeetCode vs DataDriven for Data Engineer Interviews

LeetCode's SQL problems are good practice for the core of any SQL screen. You join a schema of 2 or 3 tables and aggregate with GROUP BY and HAVING, and subqueries come up often, along with ranking and date filters. Its classics teach the habit of reading a schema carefully before writing a line: Second Highest Salary and Rank Scores, for example, or the Hard problem Trips and Users.

A data engineering SQL round adds problems that come from running pipelines. Deduplicate events that arrived twice and keep the latest version of each. Sessionize a clickstream by the gap between events. Find gaps and islands in daily activity. Build a type 2 slowly changing dimension, or write a load that can rerun without double counting. These are the questions an interviewer asks a data engineer, and they lean heavily on window functions; the SQL window functions interview questions cover that family in depth.

The other difference is the follow-up. After the query runs, a data engineering interviewer asks what happens when 2 rows tie on the ordering key, when the join key is NULL or when the table holds 5 billion rows. A green check on a fixed test case does not rehearse that conversation, and the SQL interview questions for data engineers list the follow-ups that come up most.

LeetCode's Python practice is its algorithm catalogue: 3,651 problems on arrays and trees, graphs and dynamic programming, each solvable in Python. Its daily question follows the same mix: in September 2026, 30 of 30 daily questions were algorithm problems and 0 were SQL. Its free 30 Days of Pandas plan adds 33 DataFrame problems, most of them the SQL problems rewritten in pandas.

A data engineering Python round usually hands you raw records and asks you to make them reliable: parse a log file with malformed lines, deduplicate events by key and timestamp, flatten nested JSON, merge overlapping intervals, retry a flaky API call with backoff, or stream a file too large to load at once through a generator. The interviewer is watching how you treat bad input and memory, which an algorithm problem with clean inputs never tests.

DataDriven's {COUNT:python} Python problems are written in that shape, with plain Python first and libraries second. If your loop includes a general coding round as well, the algorithm practice is still worth doing, and LeetCode remains the best place for it.

LeetCode has no Spark category and no Spark editor. A search of its problem list for Spark returns only algorithm problems whose titles happen to contain words such as sparse or span. For a data engineer interviewing at a company that runs Spark, that leaves a whole round with no practice.

A Spark round rarely stops at a correct DataFrame. 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 rest sit idle, and how partitioning on write decides how much a later query reads. DataDriven runs PySpark in the browser across {COUNT:spark} problems, a count that includes every SQL problem written for Spark, and its mock interviews ask those follow-ups once the code runs.

Every LeetCode SQL problem hands you a schema and asks you to query it. A data modeling round reverses that: the interviewer describes a business process such as rides or orders, and you design the tables live. Subscriptions are another common prompt. You decide each table's grain and keys, and how it keeps history.

The Kimball Group's 4-step dimensional design process is the order most interviewers expect you to reason in: select the business process, declare the grain, identify the dimensions, then identify the facts. Most mistakes happen at step 2, where a fact table with a vague grain double counts the moment someone joins it to a second fact.

DataDriven's {COUNT:data_modeling} data modeling problems are answered on a schema canvas. You place tables and their columns on it, then mark the primary and foreign keys and draw the relationships between them. The answer is checked for grain and keys, and for how it handles change, such as a customer who moves city.

LeetCode's problems with design in the title are class design exercises, such as implementing a data structure behind a fixed interface. A data engineering design round asks how data gets reliably from where it is produced to where it is used, and the interviewer expects a diagram.

A typical prompt is to ingest clickstream events from a mobile app and give analysts hourly session metrics. A strong answer names each stage and why it is there: events land on a queue such as Kafka so producers never wait on consumers, a job sessionizes them, a quality gate checks volume and nulls before anything is published, and late events go to a side path instead of silently changing yesterday's numbers. The interviewer then pushes on the edges: events that arrive 2 days late, a backfill after a bug, a consumer that falls behind.

DataDriven's {COUNT:pipeline_architecture} pipeline design problems are answered on a canvas built from the vocabulary of that round: sources and queues, transforms and quality gates, storage and consumers. Practising it in prose alone tends to produce answers that list tools without saying what each one guarantees.

LeetCode's best-known Premium feature is its company tags: problems marked with the companies that asked them, sortable by how often and how recently. Without Premium a problem page shows no company data at all. Premium also runs timed mock assessments built from a chosen company's tagged problems. Company tags and mock assessments both come from the same catalogue, so for most companies the tagged problems are algorithm problems.

DataDriven's problems are real data engineering interview questions, tagged with {COUNT:companies} employers and drawn from {COUNT:interviews} interview reports. Every 1 of its {COUNT:total} problems has a mock interview variant, the design rounds included, and each mock ends with a hire or no hire verdict and the reasons for it. The mock interview vs LeetCode guide explains what a spoken mock rehearses that silent practice does not.

Some large companies run a general coding round in their data engineering loop. It uses the same kind of algorithm problem a software engineering candidate gets, usually at Easy or Medium difficulty. If your recruiter confirms one, LeetCode is the best practice for it, because the catalogue is deep and every problem has an official or member-written solution. Its company tags also tell you which algorithm topics that employer favours.

LeetCode is also the right warm-up when your SQL is rusty. Its open SQL problems are free, and they are clean and quick to work through. The LeetCode SQL guide for data engineers explains which of them carry over to a data engineering round. The StrataScratch and DataLemur comparisons cover the other SQL sites data engineers use the same way.

With 6 weeks before a data engineering loop, spend week 1 on SQL basics: LeetCode's SQL 50 plan works if you have been away from SQL, and DataDriven's easier SQL problems work if you have not. Spend weeks 2 and 3 on the data engineering patterns in the dialect your target team runs, deduplication and sessionization plus slowly changing dimensions and idempotent loads.

Spend weeks 4 and 5 on the rounds LeetCode does not reach: 2 or 3 data modeling and pipeline designs a week on the canvases, and Python problems that parse and deduplicate records or stream them. Keep the last week for mock interviews of every round. If the loop has a general coding round, add LeetCode Medium problems throughout, 3 or 4 a week, chosen by the company's tags.