Accenture Data Engineer candidates commonly report project-led technical discussions, practical SQL and Spark/PySpark questions, and a later manager or HR conversation. Reported processes vary by team and stack.
$132K
Avg. Base Comp
$206K
Avg. Total Comp
2-5 rounds
Typical Rounds
2-4 weeks
Process Length
Accenture Data Engineer interviews reported here are practical and closely tied to the candidate’s own work. Prepare an end-to-end project walkthrough that clearly covers the systems and tools you used, the decisions you made, and the problems you resolved. Several candidates faced detailed follow-ups about recent projects, the scale of their data, and their familiarity with the specific technologies listed on their resumes.
SQL is a recurring technical theme. Candidates report joins, window functions, ranking, query optimization, SCD Type 2, and business-style problems such as finding the highest-paid employee by department. Practice writing and explaining a query, not just naming the relevant feature or pattern.
Spark and PySpark also recur. Reported prompts include Spark optimization techniques, PySpark coding, and explaining how these tools were used in project work. The broader platform varies: candidates encountered Azure concepts such as Unity Catalog and integration runtimes, AWS-related project questions, and GCP data-engineering services. Prepare most deeply for the stack named in the role and on your resume rather than assuming one universal toolset.
Several accounts include technical conversations followed by a manager, techno-managerial, or HR discussion. These later conversations may cover project experience, difficult situations, salary expectations, or general fit. The sequence differs across reports, and one candidate said the process lasted about two and a half weeks.
Synthesized from 19 candidate reports by our editorial team.
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Real interview reports from people who went through the Accenture process.
The most frustrating part was that the actual interview felt straightforward, but the communication afterward was not. I had two rounds. The first was a technical discussion rather than a live-coding interview, and it focused heavily on my background, current project, and core data engineering concepts. I started with a self-introduction, qualifications, and an explanation of my recent project and its architecture. From there, the interviewer asked about what a data engineer does compared with a data analyst, Medallion Architecture, how I would implement CDC, and how I would respond if corrupted data affected a warehouse. They also covered data-quality checks, ETL versus ELT, and the difference between an enterprise data warehouse and a data lake.
The technical depth was mostly fundamentals in PySpark, SQL, and data warehousing. I was asked about Spark join types, especially broadcast joins, and SQL constraints including referential integrity. There were also practical warehouse and pipeline questions, such as OLAP versus OLTP and what to provision for a scenario. Although there was no live coding session, I did get a Two Sum problem as part of the question-and-answer format. Snowflake came up as well: I was asked about tasks and streams and how to make one Snowflake task depend on another.
The second round was with a manager and was more focused on experience, how I would build a pipeline, and behavioral discussion. The interviewers generally kept the questions basic, so I would not expect a deeply algorithmic process, but you need to explain implementation choices clearly and connect fundamentals back to your project. I was initially told I had the job and waited for the offer letter and onboarding details, but about a week later I received a rejection email instead. My main takeaway is to prepare crisp project explanations alongside PySpark, SQL/data-warehouse basics, CDC, data quality, and Snowflake orchestration concepts, and to get written clarity on the outcome before assuming the process is complete.
Prep tip from this candidate
Be ready to explain your project architecture, CDC approach, and data-quality handling in detail, then drill PySpark broadcast joins, warehouse concepts such as OLAP versus OLTP, and Snowflake tasks/streams with task dependencies.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Accenture
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Largest Salary by Department | |
| Retailer Data Warehouse | |
| Top 5 Turnover Risk | |
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| Identify Managers | |
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| Missing Housing Data | |
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| Hurdles In Data Projects | |
| Target Indices | |
| Real-Time Hashtag Partitioning | |
| Digitizing Student Test Scores | |
| Count Transactions | |
| Classification and Regression | |
| Slow SQL Query | |
| Data Pipelines and Aggregation | |
| User Event Data Pipeline | |
| Bias vs. Variance Tradeoff | |
| Data Preparation for Imbalanced Data | |
| Assumptions of Linear Regression | |
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report initial recruiter or HR conversations, and some describe an aptitude, cloud-services MCQ, coding, communication, or BigQuery assessment. The assessment format and whether it appears may vary by team and role.
Candidates commonly report a technical conversation about recent projects alongside practical SQL: joins, window functions, ranking, optimization, SCD scenarios, or business-style query problems. Some accounts include hands-on SQL or Python/PySpark work.
Later technical discussions may probe Spark or PySpark architecture and optimization, ETL/ELT design, CDC or incremental loads, data quality, modeling, and the cloud tools on a candidate’s resume. Reported platforms vary across Azure, AWS, GCP, BigQuery, and Snowflake.
Candidates report a manager, techno-managerial, or HR conversation after technical evaluation. These discussions may cover ownership, collaboration, client readiness, notice period, salary expectations, and a concise explanation of past project decisions.
