Deloitte Data Engineer interviews typically mix SQL and PySpark fundamentals with cloud pipeline design across Azure, GCP, or AWS, plus HR and behavioral rounds; candidate reports show the number of stages and timeline vary by team.
$128K
Avg. Base Comp
$150K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Deloitte's Data Engineer interviews lean heavily on practical SQL and PySpark fluency rather than algorithmic puzzles. Candidates repeatedly describe classic SQL patterns: finding the second or third highest salary, the highest salary per department, and duplicate records, along with joins and window functions like rank versus dense_rank. These come alongside basic Python and PySpark tasks such as reversing a string without sorting or merging lists. One candidate had to formulate SQL verbally without seeing the schema, so practice stating your assumptions out loud.
Beyond fundamentals, deeper rounds probe how you connect cloud services to pipeline design. Depending on the team, that can mean Azure and Databricks, GCP services like GCS, Pub/Sub, and Cloud Functions alongside Airflow, or AWS paired with PySpark. Interviewers push into resume and project detail. Expect to explain why you made an architecture choice and how you would diagnose a slow Spark job. You may also be asked to join two large tables, handle the small-file problem, or implement a slowly changing dimension Type 2 or a MERGE query. Be ready to defend every component in a design you present, including data quality checks and failure handling.
Managerial and HR rounds cover motivation, why Deloitte, past projects, and general fit. One candidate also faced a presentation round with consultancy-style business questions and a deliberately confrontational tone, so prepare to defend a recommendation calmly. A few candidates describe slow or irregular communication, including one process that stretched past four months, so plan to follow up on status.
Synthesized from 16 candidate reports by our editorial team.
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Real interview reports from people who went through the Deloitte process.
Technical + functional round on RCM analytics. Questions included: How do you optimize a slow-running query? How do you handle performance issues when a dashboard is slow? How do you fix failed Glue jobs? Write a SQL query. What is your experience using AI? No surprises, I was confident.
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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 | |
| Data Pipelines and Aggregation | |
| Swap Variables | |
| Bias vs. Variance Tradeoff | |
| User Event Data Pipeline | |
| Data Preparation for Imbalanced Data | |
| Assumptions of Linear Regression | |
| Seller Type Modeling | |
| Open Source Reporting Pipeline | |
| String Palindromes | |
| Digital Classroom System Design | |
| Blob Indexing | |
| Impossibly Iterative Fibonacci | |
| Yelp-like System | |
| Text Editor With OOP |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe an HR or phone round, sometimes early and sometimes near the end. It covers why they want to join Deloitte, interest in the role, previous experience, and occasionally data engineering basics such as batch versus streaming or pipeline monitoring. These conversations are generally conversational, though one candidate reported repeated rescheduling and long gaps.
The most consistently reported stage asks candidates to write or explain SQL, including joins, window functions, finding the Nth highest salary, and duplicate records. It also includes Python or PySpark tasks like reversing a string or merging lists. Interviewers typically want a clear explanation of reasoning rather than a textbook definition. At least one candidate was asked to reason through SQL verbally without seeing the schema.
Deeper technical rounds probe end-to-end pipeline design and cloud services, depending on the team. Candidates report questions on Azure and Databricks, GCP tools like GCS, Pub/Sub, and Cloud Functions with Airflow, or AWS with PySpark. Related topics include diagnosing a slow Spark job, Spark execution concepts, SCD Type 2, factless fact tables, data quality checks, and defending architecture choices from past projects.
Final rounds typically cover background, previous projects, and general fit in a conversational format. One candidate instead had a presentation round built from a briefing pack, followed by consultancy-style business questions delivered in a deliberately confrontational tone.
