Data Engineer
IBM · Mid-level
Next: your first full round
Do the Technical Data Engineering Interview round.
One full round, as long as theirs, shows where you stand before you work on anything specific.
The rounds
Pick a round to see what it asks of you and where you stand on it.
Technical Data Engineering Interview
Test applied SQL, Python, and Spark reasoning, implementation quality, performance awareness, and production data judgment.
Coding · 40 minSet up this round
Not tried yetNo attempt yet.
What strong sounds like
- States input grain, schema assumptions, null rules, and expected output before coding.
- Selects a clear SQL, Python, or PySpark approach and explains relevant tradeoffs.
- Produces correct code for normal data and handles duplicates, nulls, and invalid types.
- Identifies shuffles, skew, partition, join, and memory risks in a practical data workload.
- Traces representative and edge inputs through the written code with accurate results.
Expect to be pushed on
Probe nulls, duplicates, late records, and empty inputs.
Ask for shuffle, partition, memory, and join tradeoffs.
Shapes of problem you may get
- SQL joins, windows, and data-quality cases
- Python collection and transformation tasks
- PySpark deduplication and type handling
- Spark memory, partitioning, and join diagnosis
How to prepare
Choose representative technical tasks
Select one SQL task, one Python task, and one PySpark task from recent work or practice. For each, write the input grain, schema assumptions, null rules, duplicate rules, and expected output before coding.
IBM reports mention SQL, Python, PySpark, Spark, ETL, modeling, and pipeline work. The exact questions for this round are not confirmed, so prepare across the stated technical areas.
Company research
Your first try takes the full 40 minutes, like the real one.
What they look for
IBM says its process varies by role and location and may include assessments, interviews, and a final decision. Recent Data Engineer candidates describe one to three technical stages, with HR and manager conversations in some loops. Common technical topics include SQL, Python, Spark or PySpark, pipelines, data modeling, cloud platforms, and production reliability. The exact format and length aren’t published.
They reward
- Applied pipeline ownership
- Production reliability and data quality
- Clear decisions with measurable outcomes
What to avoid
- Do not optimize before stating correctness and data assumptions.
- Do not skip duplicate, null, skew, or empty-input cases.
- Do not describe team output without separating your contribution.
- Do not claim improvement without a measure or comparison.
How sure we are
Worth redoing if you hear something from the recruiter that contradicts this.
Confirmed · 12
- IBM states that its hiring process can include application, expert review, one or two online assessments, an interview or assessment-center visit, and a final decision. IBM says the process can vary by role, location, and region.
Attempts
Every round you’ve done for this job, newest first.
Technical Data Engineering Interview and Managerial Project Discussion have no attempts yet.