Tredence Data Engineer interviews reported by candidates combine SQL, PySpark, Azure Databricks and Data Factory questions with deep dives into your recent projects, usually ending with an HR conversation.
$118K
Avg. Base Comp
$146K
Avg. Total Comp
2-5 rounds
Typical Rounds
Not reported
Process Length
Candidates describe Tredence's Data Engineer interview as SQL plus the Azure and Databricks stack, tested through your own project work. Several reports mention an online assessment first. Those reports cover aptitude, SQL and a coding problem, or MCQs with DSA problems. Most then describe two technical interviews and an HR conversation.
SQL appears in nearly every account. Reported topics include joins, aggregations, CTEs, window functions, self joins, duplicates and missing values. One candidate was asked about SQL's logical order of execution. Another wrote medium-level queries on pasted data, calculating sums, finding maximum sales and comparing dates. Spark questions range from optimization and DataFrame operations (split, explode, joins and group-by aggregations) to a customer-wise sum.
The Azure side is specific. Candidates report Azure Data Factory questions, including triggers. They also report Databricks topics such as Unity Catalog versus the Hive Metastore, Delta Live Tables, optimization and the Databricks Runtime. One candidate was questioned at length on Liquid Clustering in an unexpected extra technical round.
Expect to defend your latest project in detail: schema design, pipeline choices, how raw data was cleaned and validated, and what you would improve. Some candidates also saw light DSA, such as Coin Change or prime-number checks, so keep basic recursion and dynamic programming fresh. Outcomes in these reports vary, including accepted and declined offers and at least one rejection, so the process may not be uniform.
Synthesized from 8 candidate reports by our editorial team.
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Real interview reports from people who went through the Tredence process.
The technical rounds were the most challenging part of my Data Engineer interview at Tredence, especially because the questions moved between SQL, PySpark, and basic DSA rather than staying within one area. The overall process consisted of about four to five rounds. It began with an online assessment on HackerEarth that covered aptitude, SQL, and one coding problem. After clearing that, I had a communication round before moving into two technical interviews.
The first technical discussion was around 30 minutes and focused on PySpark and SQL, with some discussion of Azure Databricks. I was asked to work through SQL self joins and explain PySpark operations such as split, explode, joins, and group-by aggregations. The interviewers also went into Spark performance optimization, so it was important to understand more than just DataFrame syntax. Databricks topics included what the Databricks Runtime is and how Unity Catalog differs from the Hive Metastore. There were also questions about ADF triggers and broader data engineering concepts such as ETL, DBMS fundamentals, and data warehousing.
The later technical round included a basic DSA problem involving Coin Change, along with another PySpark DataFrame join question. The SQL portion could reach LeetCode medium-to-hard difficulty, which made time management important. I was also expected to explain my previous projects and the technologies I had used, rather than simply answer isolated theory questions. The panel was positive and the process itself was smooth, but the breadth of the technical coverage made it demanding. The final HR discussion covered salary and company policies. I received an offer but ultimately declined it. My main takeaway is to prepare SQL and PySpark together, with particular attention to Spark optimization and the surrounding Azure data stack, while keeping basic dynamic programming fresh for the coding portion.
Prep tip from this candidate
Practice SQL self joins and medium-to-hard SQL problems, plus PySpark split, explode, joins, and group-by aggregations. Review Spark optimization, Databricks Runtime, Unity Catalog versus Hive Metastore, ADF triggers, and the Coin Change problem.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Tredence
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Minimum Change | |
| Hurdles In Data Projects | |
| Cumulative Sales By Product | |
| RAG Strict Source Control | |
| Count Transactions | |
| Bias vs. Variance Tradeoff | |
| String Palindromes | |
| Pipeline Transformation Failures | |
| Fixed-Length Arrays: Deletion | |
| Testing Constraints | |
| Your Strengths and Weaknesses | |
| Analyzing Multiple Data Sources | |
| Employee Salaries | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Closest SAT Scores | |
| Prime to N | |
| Merge Sorted Lists | |
| Experiment Validity | |
| Largest Salary by Department | |
| Top 3 Users | |
| Find the Missing Number | |
| String Shift | |
| Last Transaction | |
| Maximum Profit | |
| Third Purchase | |
| First Touch Attribution | |
| Retailer Data Warehouse | |
| Size of Joins |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report starting with an online test. One describes HackerEarth with aptitude, SQL and one coding problem. Another describes MCQs plus two coding problems, one moderate and one advanced. Not every account mentions this step, so it may depend on the hiring channel.
Candidates typically report a resume walkthrough followed by SQL on joins, aggregates, CTEs and window functions. Some also mention PySpark operations, Databricks basics, or small DSA problems. One candidate described rounds of about 30 minutes, so concise answers help.
Candidates report deeper PySpark and Databricks questions, including optimization, Unity Catalog, Delta Live Tables, slowly changing dimensions, and DataFrame joins. Some got a Coin Change style problem. One candidate instead got analytical and estimation questions from a senior interviewer. Project design decisions are a recurring theme.
Candidates describe a short HR conversation, around 10 minutes in one account. It covers why you want to join Tredence, your goals, and strengths and weaknesses, and sometimes salary or onboarding. One report also mentions company-knowledge questions, so it may help to read about Tredence beforehand.
