Oracle Data Engineer candidates report timed coding screens, SQL and data modeling questions, project deep dives, and a closing HR conversation. Details vary by candidate and team.
$129K
Avg. Base Comp
$178K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Oracle's Data Engineer interviews differ from candidate to candidate, so preparation works best when it covers fundamentals rather than one tool. All five reports ended without an offer, which makes the details they share useful for seeing where the bar sat.
Coding comes early for most. One candidate on the Oracle Health data engineering side had a one-hour HackerRank session that opened with about ten minutes of behavioral questions and then moved to an integer-to-Roman-numeral conversion, where the subtractive cases mattered. Another sat an online assessment with arrays, strings and dynamic programming, including a longest zero-sum subarray problem, plus multiple-choice questions on DBMS, OS and networking. A third had two 15-minute timed questions, a sorting problem followed by SQL.
SQL and modeling show up across the loop. Reported questions include WHERE versus HAVING, join types, and star versus snowflake schemas. One candidate saw trees and graphs in a first technical round, another was asked about OCI components, and a third got scenario questions on cloud infrastructure and Airflow.
Later rounds lean on your own work. One candidate was asked about the limitations of past projects and how to improve them. The same round also covered machine learning questions on bias-variance tradeoff, regularization and transformers, and a later managerial round asked how to debug a production performance bottleneck. Another said the project round probed whether their interest in cloud technology was genuine. One candidate described unclear communication from HR about what each round would cover, so ask your recruiter early.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Oracle process.
The hardest part of my Oracle interview was actually how straightforward it was. The role was for a Senior Software Engineer on the Data Engineering side at Oracle Health, and a recruiter reached out to me on LinkedIn with a link to apply. After I submitted my application, I had an initial screening call that was mostly about my background, high-level fit for the role, and a few basic behavioral questions.
After that, I was invited to a 1-hour technical interview on HackerRank. The first 10 minutes were behavioral, and then we moved into a LeetCode-style coding problem. I was allowed to use my preferred language. The coding question was to take an integer and convert it to the equivalent Roman numeral, with examples like 1 to I, 49 to XLIX, and 23 to XXIII. It was pretty direct and not especially difficult algorithmically, but I did need to be careful with the subtractive notation rules. The process felt more like checking fundamentals and clean implementation than testing anything deeply data-engineering specific. I didn’t get an offer, so my main takeaway is to be ready for a short behavioral intro followed by a simple but precise coding exercise on HackerRank.
Prep tip from this candidate
Practice integer-to-Roman numeral conversion, especially the subtractive cases like 4, 9, 40, and 90, since that was the core coding task. Also be ready to spend the first few minutes on concise behavioral background before jumping into code.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Oracle
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Closest SAT Scores | |
| Subscription Overlap | |
| Merge Sorted Lists | |
| String Shift | |
| Upsell Transactions | |
| Largest Salary by Department | |
| Minimum Change | |
| Rectangle Overlap | |
| Integer to Roman | |
| Find the First Non-Repeating Character in a String | |
| Size of Joins | |
| The Brackets Problem | |
| Normalize Grades | |
| Scrambled Tickets | |
| Random Forest Explanation | |
| Resumable Fact Table Load | |
| Cyclic Detection | |
| Project Pairs | |
| Sort Strings | |
| Valid Anagram | |
| Finding The Mode | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Target Indices | |
| Priority Queue Using Linked List | |
| Prime Numbers Identification | |
| Move Zeros Back | |
| Nearest Common Ancestor | |
| Count Transactions |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates describe an initial screening round. One came after LinkedIn outreach from a recruiter and was mostly about background, high-level fit and a few basic behavioral questions. Another candidate said HR was not clear about what each later round would cover, so it is worth asking the recruiter for the structure up front.
Candidates report timed coding in different forms. One had 2-3 DSA questions on arrays, strings and dynamic programming plus MCQs on DBMS, OS and networking. Another had a one-hour HackerRank session with a Roman numeral conversion. A third had a 15-minute sorting problem followed by a 15-minute SQL question. Practice careful, clean implementation.
Technical rounds covered a mix of topics depending on the candidate. Reported questions include trees and graphs, WHERE versus HAVING, join types, star versus snowflake schemas, basic Java and OCI components. Another candidate was given scenario questions on cloud infrastructure and Airflow. Expect SQL and data warehouse design alongside core algorithms.
Candidates report being asked to walk through past projects and explain their technical decisions. One was also asked about project limitations and how to improve them in a round that included machine learning questions, and later faced a production bottleneck debugging scenario in a managerial round. Another was assessed on genuine interest in cloud technology.
Later conversations were behavioral or HR-focused. One candidate described a final behavioral round, another covered relocation, CTC expectations, availability and career goals, and a third was asked why Oracle, about sponsorship and which teams interested them. One candidate found turnaround between rounds slow and feedback timing unclear.
