Deloitte Data Engineer candidates often describe two technical rounds plus a managerial or HR conversation. The rounds mix SQL, Python or PySpark, cloud pipeline scenarios and project deep-dives.
$108K
Avg. Base Comp
$137K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
The Deloitte Data Engineer interview comes across as practical and tied to a specific stack rather than algorithm-heavy. Several candidates describe three rounds, typically two technical interviews followed by a managerial, behavioral or HR conversation. Others mention only their technical rounds or simply describe multiple rounds.
SQL turns up in many accounts. The second-highest salary problem is mentioned more than once, along with window functions, rank versus dense_rank, duplicate detection, CASE WHEN logic, joins, MERGE and slowly changing dimension Type 2. Python questions include removing duplicates from a list of dictionaries, reversing a string and merging lists.
The heavier technical discussion often moves to PySpark and cloud pipelines. Candidates mention Spark optimization, diagnosing a slow Spark job, the small-file problem, joining large tables, incremental loads and dimensional modeling. The cloud stack differs by team: candidates report Azure and AWS, GCP with Airflow, or Databricks and Delta tables.
Project depth is the common thread. Interviewers walk through resume projects and ask why you made each design choice, so rehearse a start-to-finish pipeline walkthrough covering sources, transformations, testing, monitoring and failure handling. The last stage is usually managerial or behavioral, covering motivation, background and fit. One candidate also describes irregular updates and having to follow up for status.
Synthesized from 19 candidate reports by our editorial team.
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Real interview reports from people who went through the Deloitte process.
The thing I'd tell anyone prepping for the Deloitte data engineer loop is that the technical bar is real but not exotic. The process was three rounds, and the first was a coding screen covering Python and SQL at an easy-to-medium level. On the Python side I was asked to remove duplicates from a list of maps, which is the kind of reshaping you'll do constantly in pipeline code, so get comfortable working with dictionaries inside lists. The SQL question asked for conditional output: if a value in one column matched a condition, return one column's value, otherwise return another's. That's a CASE WHEN problem, so make sure it's second nature.
The second round was the one I spent the most time on, and it was less about puzzles and more about my past work. The interviewer went through my projects in depth, and since the role is data engineering focused, a good stretch of it covered ETL and the tools I'd used to move data. I also got questions on ETL concepts, basic testing, object-oriented programming, and Python libraries like NumPy for data analysis. Be ready to walk through a project end to end: where the data came from, how you transformed it, how you checked it was correct, and what you'd change now.
The final round was behavioral, with senior managers. It felt more like a conversation about how I work and why I wanted to join than a test, though I'd still prepare concrete examples. I received an offer afterward. The process rewarded people who could explain their own projects clearly as much as people who could code.
Prep tip from this candidate
Drill CASE WHEN-style conditional SQL and removing duplicates from lists of dictionaries in Python, then rehearse a walkthrough of one pipeline project covering sources, transformations, testing, and tools, since the middle round centered heavily on that.
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Topics based on recent interview experiences.
Featured question at Deloitte
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Merge Sorted Lists | |
| Maximum Profit | |
| Size of Joins | |
| Real-Time Transaction Streaming | |
| Normalize Grades | |
| Cyclic Detection | |
| Skyscanner Partner ETL | |
| Missing Housing Data | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Portfolio Platform Architecture | |
| Duplicate Rows | |
| Using R Squared | |
| Classification and Regression | |
| Slow SQL Query | |
| Swap Variables | |
| Data Pipelines and Aggregation | |
| User Event Data Pipeline | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Assumptions of Linear Regression | |
| Seller Type Modeling | |
| String Palindromes | |
| Open Source Reporting Pipeline | |
| Digital Classroom System Design | |
| Impossibly Iterative Fibonacci | |
| Blob Indexing | |
| Yelp-like System | |
| Text Editor With OOP |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates describe easy-to-medium Python and SQL, such as CASE WHEN logic, removing duplicates from a list of dictionaries, second-highest salary and window functions. Some also mention PySpark and scenario-based ETL questions. Interviewers seem to care most about how clearly you explain your approach.
A deeper round on past projects, architecture, ETL tools and testing is common. Depending on the team, it can also cover Spark performance tuning, Azure, AWS or GCP services, Airflow, dimensional modeling and incremental loads. Be ready to defend your design decisions and say what you would change.
The final stage is described as a behavioral conversation with senior managers, a managerial round on background and projects, or an HR round. Reported topics include motivation, previous experience and general fit. Bring concrete examples and clear reasons for wanting the role.
