PwC Data Engineer candidates describe an interview that emphasizes practical SQL and Python, with some reports also covering Azure, PySpark, prior experience, and HR discussions.
$140K
Avg. Base Comp
$202K
Avg. Total Comp
2-4 rounds
Typical Rounds
2-4 weeks
Process Length
PwC Data Engineer interview reports point to a practical, fundamentals-led preparation path. One candidate described a four-round sequence with two technical conversations, followed by director and HR discussions. Their technical preparation centered on SQL window functions and min-max problems, Python list and string coding, PySpark data manipulation, and Azure services including triggers, Data Factory, and storage. The director conversation covered prior experience and certifications, while the HR discussion addressed numbers and location.
Another candidate reported a different, shorter sequence: an HR cultural-fit conversation followed by a technical meeting with a team lead and senior data engineer or scientist. That technical discussion focused on SQL joins, built-in functions, query writing, database concepts, query optimization, and Python specifics. They characterized the process as straightforward and generally fundamental rather than algorithm-heavy, while noting that some questions were more challenging than expected.
Prepare to explain how you approach practical query work, not merely produce syntax. Be ready to discuss Python choices clearly and connect your past projects or certifications to the data-engineering tools you have used. The reports are limited, but together they consistently place SQL and Python at the center of preparation.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Pwc process.
I had 4 rounds of interview. 2 technical, 1 director and 1 HR round. First two rounds were focused on Azure, Python, SQL, pySpark Coding, Director round had questions on previous experience and certifications and HR round was fully focused on numbers, location etc.
Overal medium difficulty.
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Topics based on recent interview experiences.
Featured question at Pwc
Write a query to return the two students with the closest test scores and the score difference
| Question | |
|---|---|
| Experiment Validity | |
| Size of Joins | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Slow SQL Query | |
| Data Pipelines and Aggregation | |
| Data Preparation for Imbalanced Data | |
| Electricity Supply | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| User Journey Analysis | |
| Data Cleaning Experiences | |
| Clustering Basketball Players | |
| Creating Companies Table | |
| Third Party Ad Pricing | |
| 2nd Highest Salary | |
| Rolling Bank Transactions | |
| Merge Sorted Lists | |
| Longest Streak Users | |
| Top 3 Users | |
| Maximum Profit | |
| Find the First Non-Repeating Character in a String | |
| Skyscanner Partner ETL | |
| Real-Time Transaction Streaming | |
| Normalize Grades | |
| Resumable Fact Table Load | |
| Cyclic Detection | |
| Missing Housing Data | |
| Duplicate Rows |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an HR conversation that may assess communication and general background rather than deep behavioral questioning. One four-round account also says HR focused on numbers and location, so be prepared to discuss your background and practical preferences clearly.
Candidates report SQL joins, built-in functions, query writing, database concepts, query optimization, and Python-specific questions. One account describes list and string coding, while another emphasizes explaining practical query decisions rather than handling an algorithm-heavy screen.
In one reported four-round process, technical interviews included PySpark data-manipulation coding plus Azure triggers, Azure Data Factory, and storage. These topics may vary by team, but candidates with relevant experience should be ready to explain applied work with them.
One candidate reports a director round after two technical interviews. That conversation focused on previous experience and certifications, so applicants may benefit from concise examples that connect their project choices, responsibilities, and credentials to data-engineering work.
