Accenture Data Engineer interviews center on hands-on SQL and PySpark rounds, a detailed walkthrough of your own pipeline projects, and a separate behavioral/managerial round, with cloud-stack questions (Azure, AWS, GCP, or Snowflake) varying by project.
$115K
Avg. Base Comp
$140K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-4 weeks
Process Length
Accenture's Data Engineer interviews consistently center on hands-on SQL and PySpark fluency paired with a detailed walkthrough of your own project work, rather than abstract algorithm puzzles. Across candidate reports, technical rounds return repeatedly to the same core: window functions, query optimization, joins, and SCD Type 1/Type 2 dimensions in SQL, alongside PySpark and Spark architecture questions such as broadcast joins or coalesce versus repartition. Cloud content varies by project team: some candidates were asked about Azure Data Factory, Databricks, and Synapse; others about AWS Glue, Lambda, and EMR sizing; others about GCP/BigQuery, including a timed 30-question BigQuery assessment; and a few about Snowflake tasks and streams or Kafka/Flink internals. Because the stack differs by engagement, it's worth reviewing whichever cloud platform sits on your own resume rather than trying to cover every vendor at once.
Beyond tooling, interviewers repeatedly ask candidates to narrate a real pipeline end to end: incremental loads, CDC or watermarking, data-quality checks, and how a production failure was diagnosed and resolved. This project-first framing shows up whether the eventual outcome was an accepted offer or a rejection, suggesting delivery experience is weighed alongside raw technical correctness. A separate managerial or techno-managerial round then shifts toward behavioral territory - handling a difficult project situation, client communication, teamwork - and HR typically closes the loop with compensation, notice period, and location details.
Round counts and timelines vary by report: some candidates describe the process as two rounds (often a technical discussion plus HR), while others describe three, such as two technical rounds followed by a manager round. End-to-end timing ranges from roughly 10-15 days to about a month, depending on scheduling. A few candidates also mention receiving an offer for a different role or location than originally discussed, so it's worth confirming those specifics once you reach the offer stage.
Synthesized from 34 candidate reports by our editorial team.
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Real interview reports from people who went through the Accenture process.
This was a short process for a data engineer internship at Accenture, and it ended without an offer. It began with an HR screening call covering my background, my studies, my availability, and why I wanted the internship. The technical round followed and was mostly about data engineering concepts, SQL, Python, and my previous projects. The atmosphere was friendly, and the questions stayed close to what the role would involve.
The SQL and Python questions were the most concrete part. The SQL portion asked about the different types of joins, and I also worked through an easy LeetCode-style join problem. The Python portion was another easy LeetCode-level question. Neither was hard on its own, so the bar seemed to be whether I could explain my reasoning clearly and reach a working answer. The technical round was conducted over Teams. I received positive feedback afterward.
The final decision, though, depended on the hiring manager and on project availability, so a good technical showing didn't settle the outcome. I did not receive an offer. The takeaway is that even a solid technical interview may not lead to a role when team needs are uncertain, so it's worth keeping other applications active while you wait.
Prep tip from this candidate
Be ready to explain the different types of joins in SQL clearly, since that concept came up directly, and practice easy LeetCode-style join and Python problems you can talk through out loud.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with an online application or LinkedIn outreach followed by a recruiter or HR screening call covering background, availability, and role fit. This stage is typically conversational rather than technical, though one candidate was asked directly what salary would prompt them to switch companies during this early call, and another discussed compensation expectations directly with HR before the technical rounds began.
Some candidates describe an online assessment stage, such as a timed BigQuery quiz (30 questions in roughly 40 minutes), an AWS core-services multiple-choice screen, or a video/coding round on a platform like HirePro. These assessments typically emphasize SQL, Python, and cloud/data-platform fundamentals rather than open-ended algorithm design, and more than one candidate reported real time pressure at this stage.
The core of the process is typically one or two technical rounds combining SQL (joins, window functions, query optimization, SCD Type 1/2), PySpark and Spark architecture concepts (such as broadcast joins or coalesce versus repartition), and a detailed walkthrough of the candidate's own data engineering projects. Depending on background, questions may also cover Azure Data Factory/Databricks/Synapse, AWS Glue/Lambda/EMR, GCP/BigQuery, Kafka/Flink, Hive, or Snowflake tasks and streams, so tailoring prep to your own resume matters more than covering every platform.
Several candidates describe a separate managerial round focused on ownership, teamwork, handling a difficult project situation, and client-facing communication rather than deep technical content. A few reports note this round can feel more evaluative on fit than the earlier technical stages, so having a clear, concrete story about a real project challenge is worth preparing in advance.
The process typically closes with an HR conversation covering compensation, notice period, location, and offer logistics. Candidates report mixed outcomes here: some received prompt offers and accepted quickly, while others waited weeks for a decision, were offered a different role or location than expected, or were told to wait several months before reapplying after a rejection.
