Accenture Data Engineer candidates describe project-led interviews that test practical SQL, PySpark or Spark, cloud tooling, and pipeline judgment. Reported processes span two to three rounds and about one to four weeks.
$130K
Avg. Base Comp
$155K
Avg. Total Comp
2-3 rounds
Typical Rounds
1-4 weeks
Process Length
Accenture Data Engineer interviews in these reports are most useful to approach as discussions of how you have built and operated data systems, not solely as coding tests. Several candidates describe project walkthroughs alongside practical SQL and platform questions. Be ready to connect a technical choice to the data problem it solved: explain your pipeline responsibilities, the services or frameworks you used, and how you handled tradeoffs, optimization, or production issues.
SQL appears repeatedly, from joins, GROUP BY, and window functions to SCD scenarios and a query for the leading product category or employee within a group. Practice writing and explaining queries, including why a particular join, window, or grouping method is appropriate. Spark and PySpark are also recurring: candidates reported optimization methods, coalesce versus repartition, broadcast hash joins, resource allocation, and hands-on PySpark code. One candidate described a 16-minute first technical conversation, so concise explanations may matter as much as breadth.
The stack varies by role or project. Individual reports mention AWS services and EMR, Azure concepts, BigQuery, Airflow, dimensional modeling, data warehousing, and Unix. Prepare the tools on your own resume in depth rather than assuming one cloud platform will apply. Candidates also reported manager or HR conversations after technical work, where project challenges, consulting experience, fit, or practical judgment could be discussed. Explicit end-to-end reports ranged from about 10–15 days to roughly one month, while the number and arrangement of stages varied.
Synthesized from 9 candidate reports by our editorial team.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report several entry paths: an HR conversation, an online assessment, or an AWS-focused multiple-choice screen. Prepare a concise introduction to your background and be able to relate recent data-engineering work to the role; the exact starting format varied across reports.
Candidates report technical questions on SQL joins, GROUP BY, window functions, query optimization, and SCD scenarios, plus Spark or PySpark topics such as optimization, partitioning, coalesce versus repartition, and broadcast joins. One report described this discussion as implementation-focused rather than algorithm-heavy.
Several candidates describe resume- or project-led conversations. Depending on the assignment, reports mention cloud services, BigQuery, Airflow, dimensional modeling, data warehousing, and pipeline management. Be prepared to explain the scale, architecture, tradeoffs, and troubleshooting behind work you personally delivered.
Some candidates report a manager round or a final in-office discussion after earlier technical stages. These conversations may revisit project challenges, consulting experience, and practical technical judgment. Two reports explicitly described three rounds, while another explicitly described two, so later-stage structure varies.
