78 problems across 5 domains, plus 4 full mock interview loops. This plan assumes 1.5 to 3 hours per day (depending on the week) and some prior SQL and Python experience. By week 8, you should be solving Medium problems in under 15 minutes and delivering structured system design walkthroughs in 30 minutes.
Weeks total
Practice problems
Domains covered
Full mock loops
Most candidates prepare for data engineering interviews the same way: they open a question list, solve random problems, and hope for the best. This approach has two problems. First, you spend too much time on domains you already know (usually SQL) and too little on domains you don't (usually data modeling and pipeline architecture). Second, you never build the stamina to perform under the time pressure of a real interview loop.
A structured plan solves both problems. It allocates your time proportionally to interview frequency: SQL gets 2 weeks because it appears in 41% of DE interview questions. Python gets 2 weeks because it appears in 35%. Data modeling gets 1 week (18%). Pipeline architecture gets 1 week (3%, but it appears in nearly all senior interviews). Spark gets 1 week for roles that require it. Week 8 is exclusively full mock interviews to build stamina and time management skills.
The plan is organized around weekly milestones, not just problem counts. Solving 15 SQL problems means nothing if you can't solve them under time pressure. Each week includes specific benchmarks that tell you whether you are ready to move on or need more practice in the current domain.
This plan is designed for candidates with at least 6 months of professional experience with SQL and Python. You should be able to write a basic SELECT with WHERE and GROUP BY without looking up the syntax. You should know what a dictionary is in Python and how to iterate over a list.
If you are starting from zero, add 4 weeks of fundamentals before this plan: 2 weeks of SQL basics (SELECT, WHERE, GROUP BY, JOINs) and 2 weeks of Python basics (data types, loops, functions, file I/O). DataDriven's Learn section covers these fundamentals with interactive lessons.
You do not need prior experience with Spark, data modeling, or pipeline architecture. Weeks 5 to 7 build these skills from scratch. But if you have experience in these areas, you can accelerate those weeks and spend more time on mock interviews.
Topics
Daily Breakdown
Day 1 to 2: JOINs (5 problems). Day 3 to 4: GROUP BY and aggregation (5 problems). Day 5 to 7: Subqueries and combined patterns (5 problems). Run every query on DataDriven. Don't skip to the solution. If you're stuck for more than 10 minutes, look at the hint, not the answer.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: ranking functions (4 problems). Day 3 to 4: LAG/LEAD and running calculations (5 problems). Day 5: recursive CTEs (3 problems). Day 6 to 7: mixed window function problems at Medium difficulty (3 problems). By end of week 2, you should solve Medium SQL problems in under 15 minutes consistently.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: JSON parsing and flattening (3 problems). Day 3 to 4: file processing with generators (3 problems). Day 5 to 7: collections and mixed patterns (4 problems). Write all code in the DataDriven editor. The AI grader catches style issues that you won't notice yourself.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: sessionization and time-window logic (3 problems). Day 3 to 4: retry and error handling patterns (3 problems). Day 5 to 7: validation, change detection, and production patterns (4 problems). These problems map directly to what you do on the job. Interviewers love candidates who write production-quality code in interview settings.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: star schema design for e-commerce and ride-sharing (3 exercises). Day 3 to 4: SCD handling and history tracking (3 exercises). Day 5 to 6: data vault and medallion architecture (2 exercises). Day 7: mixed modeling exercises with trade-off discussions (2 exercises). Practice explaining your design choices out loud. Modeling interviews are 60% communication.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: design a clickstream pipeline (1 design) and a multi-source API ingestion pipeline (1 design). Day 3 to 4: migration planning and optimization (2 designs). Day 5 to 6: monitoring and failure handling (2 designs). Day 7: mixed architecture designs (2 designs). Use a whiteboard or drawing tool. Practice structuring your answer: requirements, high-level design, deep dive, trade-offs.
End-of-Week Milestones
Topics
Daily Breakdown
Day 1 to 2: DataFrame transformations and window functions (3 problems). Day 3 to 4: join optimization and skew handling (3 problems). Day 5 to 6: Delta Lake operations (2 problems). Day 7: Structured Streaming (2 problems). Run all code on DataDriven's PySpark environment. Reading about Spark is not the same as writing Spark code.
End-of-Week Milestones
Topics
Daily Breakdown
Mock interview structure: Round 1 (SQL, 45 min), 15-min break, Round 2 (Python, 45 min), 15-min break, Round 3 (System Design, 45 min), 15-min break, Round 4 (Behavioral, 30 min). Use DataDriven's mock interview simulator to automate question selection, timing, and AI feedback. Run 2 full loops per week. Review your weakest round each day between loops.
End-of-Week Milestones
No plan survives contact with reality. Here are the three most common adjustments and when to make them.
You are missing milestones. If you can't hit the weekly milestones by the end of the week, spend 2 to 3 extra days on that domain before moving on. It's better to finish the plan in 9 to 10 weeks with solid skills than to rush through in 8 weeks with gaps. Data engineering interviews test depth, not breadth. A shallow understanding of 5 domains is less valuable than deep mastery of 3.
You are ahead of schedule. If you hit all milestones by day 5 of a week, use the remaining 2 days to start Hard problems in that domain. Hard problems appear in senior and staff-level interviews. If you are targeting L5+ roles, you need to be comfortable with Hard difficulty.
Your target company emphasizes a specific domain. If the job description mentions Spark heavily, consider swapping weeks 6 and 7 so you spend more time on Spark. If the company is known for behavioral interviews (Amazon, for example), add a behavioral prep component to week 8. If the role is data modeling heavy (analytics engineer positions), double the time on week 5 and reduce Python.
Consistency beats intensity. Here is a daily routine that fits into a working schedule.
Review yesterday's mistakes. Read the AI feedback on your previous submissions. Identify one pattern you got wrong and resolve to watch for it today.
Solve 2 to 3 new problems from the current week's domain. Set a timer for each problem. If you're stuck after 10 minutes, read the hint. If you're stuck after 20 minutes, study the solution and solve it again from scratch tomorrow.
Review the AI grader feedback on today's submissions. Write down one thing you learned in a notebook (physical or digital). This five-sentence summary cements the pattern in long-term memory.
Speed drill: solve one Easy problem from a previous week's domain as fast as possible. This maintains skills you've already built while you learn new ones. Track your solve time and try to beat yesterday's record.
You can solve any Medium SQL problem (JOINs, window functions, CTEs) in under 15 minutes with correct handling of NULLs and edge cases. If not, spend week 3 on SQL instead of Python.
You can write a Python function that processes a file lazily, handles errors gracefully, and includes type hints. You can explain your code while writing it. If not, extend Python by 3 to 4 days.
You can design a star schema for an unfamiliar domain in 20 minutes and draw a pipeline architecture diagram in 15 minutes, explaining trade-offs at each layer. If not, extend by 3 to 4 days.
You complete a full 4-round mock interview and receive 'Strong Hire' on 3+ rounds. Your SQL and Python solve times are under 15 minutes for Medium problems. You explain your approach clearly without long pauses.
1. Spending all your time on SQL. SQL is the most tested domain (41%), but candidates who only practice SQL fail Python and modeling rounds. Stick to the plan's time allocation.
2. Skipping mock interviews. Individual problem practice builds skills. Mock interviews build performance ability. Many candidates skip week 8 because they feel "not ready." You will never feel ready. Do the mock interviews anyway. That's how you become ready.
3. Reading solutions without writing code. If you read a solution and think "I would have gotten that," you are fooling yourself. The only way to verify that you can solve a problem is to solve it. On a blank screen. With a timer. Every time.
4. Not practicing communication. Data engineering interviews are not just coding tests. Interviewers evaluate how you explain your approach, ask clarifying questions, and discuss trade-offs. Practice thinking out loud while you code. It feels awkward at first. It becomes natural by week 4.
5. Ignoring your weakest domain. Your brain wants to practice what you are already good at. It feels productive. It isn't. Your weakest domain is your highest-impact area for improvement. If you dread data modeling, that's exactly where you need to spend more time.
Every problem runs on real infrastructure with AI grading. Start week 1 today.