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30 questionsMedium difficulty6 rounds

Databricks Qa Automation Engineer Sdet Interview Questions (2026)

30 real Qa Automation Engineer Sdet interview questions compiled for Databricks, 30 of them tailored to Databricks's actual interview flavor. Below: the interview process, the questions with answer outlines, the topics tested, and how to prepare.

Databricks is notorious for one of the hardest pure-coding bars in the industry: phone screens and onsite coding rounds regularly use LeetCode-hard problems demanding fully working, tested code, followed by deep distributed-systems design given its Spark heritage. The Bengaluru R&D office holds the same bar as San Francisco, and many strong candidates fail on speed-to-correct-code.

Questions

30

30 company-tailored

Difficulty

Medium

from our question mix

Rounds

6

typical loop

Role

Qa Automation Engineer Sdet

interview prep

Databricks's interview process

  1. 1Recruiter Screen30 minEasy

    Role calibration and an honest preview of the coding difficulty; sets expectations for the loop.

  2. 2Coding Phone Screen60 minHard

    One LeetCode hard-leaning problem to complete, working code with edge cases handled - interviewer runs the code mentally or literally.

  3. 3Onsite Coding I & II60 minHard

    Two more hard implementation rounds; problems often disguise systems concepts (LRU variants, schedulers, query planners) requiring airtight code.

  4. 4Distributed System Design60 minHard

    Design a data-infrastructure system (distributed query engine, job scheduler, storage layer) with deep follow-ups on failure modes and data layout.

  5. 5SQL & Data Engineering Round60 minHard

    For data/field roles: Spark/SQL optimization, partitioning strategy and pipeline debugging on realistic lakehouse scenarios.

  6. 6Hiring Manager Round45 minMedium

    Project deep-dive doubling as the behavioral round - motivation, ownership, and technical judgment interrogated through your past work.

Qa Automation Engineer Sdet interview questions asked at Databricks

  1. Q1

    How would you test a REST API endpoint for Databricks's seller onboarding flow with authentication, pagination, and error responses?

    MediumAPI/UI automationAPI testingDatabricks-specific

    Context: Databricks seller onboarding flow

    How to answer: Cover contract checks, status codes, auth failures, paging boundaries, and schema validation. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  2. Q2

    How would you integrate automated tests for Databricks's admin approval workflow into CI/CD without slowing every pull request?

    MediumDebugging and test strategyCI/CD test executionDatabricks-specific

    Context: Databricks admin approval workflow

    How to answer: Use suite tiers, parallelism, ownership, quarantines, and release-blocking criteria. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  3. Q3

    When would you choose Cypress over Playwright or Selenium for Databricks's login and account recovery flow, and what tradeoffs would you call out?

    MediumAutomation codingCypressDatabricks-specific

    Context: Databricks login and account recovery flow

    How to answer: Compare browser coverage, debugging, network control, speed, and CI behavior. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  4. Q4

    Explain Page Object Model for Databricks's search and filtering experience. When does it help, and when does it become a bad abstraction?

    MediumAutomation codingPage Object ModelDatabricks-specific

    Context: Databricks search and filtering experience

    How to answer: Keep page APIs user-intent oriented and avoid hiding assertions or business rules. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  5. Q5

    How would you debug a flaky Playwright test in Databricks's order tracking workflow?

    MediumAutomation codingPlaywrightDatabricks-specific

    Context: Databricks order tracking workflow

    How to answer: Discuss locators, waits, traces, retries, isolation, network timing, and deterministic assertions. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  6. Q6

    How would you use Postman/Newman in CI for Databricks's notification preference center without making the suite hard to maintain?

    MediumAPI/UI automationPostman/NewmanDatabricks-specific

    Context: Databricks notification preference center

    How to answer: Use environments, data files, assertions, secret handling, and useful reports. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  7. Q7

    Design REST API tests for create, update, and delete operations in Databricks's admin approval workflow

    MediumAPI/UI automationREST testingDatabricks-specific

    Context: Databricks admin approval workflow

    How to answer: Check idempotency, validation, permissions, response shape, and audit events. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  8. Q8

    Which SQL checks would you write to validate data created by Databricks's login and account recovery flow?

    MediumAPI/UI automationSQL for testingDatabricks-specific

    Context: Databricks login and account recovery flow

    How to answer: Check records, joins, statuses, timestamps, aggregates, and reconciliation with UI/API results. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  9. Q9

    How would you handle dynamic elements in Selenium or Playwright for Databricks's checkout journey?

    MediumAutomation codingSeleniumDatabricks-specific

    Context: Databricks checkout journey

    How to answer: Prefer stable locators, explicit waits, app test IDs, and avoiding brittle sleeps. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  10. Q10

    What accessibility checks would you include for Databricks's seller onboarding flow?

    MediumDebugging and test strategyaccessibility testing basicsDatabricks-specific

    Context: Databricks seller onboarding flow

    How to answer: Cover keyboard navigation, labels, focus order, contrast, screen-reader basics, and automated plus manual checks. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  11. Q11

    Write the structure of a high-quality bug report for a failure in Databricks's order tracking workflow

    MediumDebugging and test strategybug reportingDatabricks-specific

    Context: Databricks order tracking workflow

    How to answer: Include environment, steps, expected versus actual, evidence, severity, and business impact. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  12. Q12

    How would you work with developers when automation exposes recurring defects in Databricks's order tracking workflow?

    EasyBehavioral/project discussioncross-functional communicationDatabricks-specific

    Context: Databricks order tracking workflow

    How to answer: Focus on shared ownership, actionable reports, root cause, and prevention. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  13. Q13

    How should a QA automation engineer handle a defect from discovery through closure for Databricks's checkout journey?

    MediumDebugging and test strategydefect lifecycleDatabricks-specific

    Context: Databricks checkout journey

    How to answer: Cover triage, reproduction, ownership, retest, regression coverage, and communication. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  14. Q14

    Design an end-to-end test for Databricks's checkout journey. What would you automate, what would you mock, and what would you leave manual?

    MediumAPI/UI automationend-to-end testingDatabricks-specific

    Context: Databricks checkout journey

    How to answer: Focus on a few critical journeys, stable data, and readable failure output. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

  15. Q15

    A test for Databricks's login and account recovery flow fails only in CI. How would you investigate and fix it?

    HardDebugging and test strategyflaky test debuggingDatabricks-specific

    Context: Databricks login and account recovery flow

    How to answer: Compare local versus CI state, browser versions, timing, data, parallelism, and logs. State the test objective, risk, and the user or system behavior being protected. Describe automation scope, data setup, assertions, observability, and CI ownership. Call out tradeoffs, failure modes, and how you would keep the suite maintainable.

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Topics tested most

API testing1
CI/CD test execution1
Cypress1
Page Object Model1
Playwright1
Postman/Newman1
REST testing1
SQL for testing1

How to prepare for the Databricks Qa Automation Engineer Sdet interview

Know Spark/distributed data deeply; strong coding; prepare data-platform design

Frequently asked questions

How hard is the Databricks Qa Automation Engineer Sdet interview?

Based on our bank of 30 Qa Automation Engineer Sdet questions asked at Databricks, the overall difficulty is medium (Databricks's process is generally rated extreme). Expect around 6 rounds spanning API testing, CI/CD test execution, Cypress.

How many interview rounds does Databricks have for a Qa Automation Engineer Sdet?

Databricks typically runs about 6 rounds for Qa Automation Engineer Sdet candidates: Recruiter Screen → Coding Phone Screen → Onsite Coding I & II → Distributed System Design → SQL & Data Engineering Round.

What is the interview process at Databricks?

The Databricks interview process typically runs: Recruiter screen -> technical screen -> onsite (coding, distributed-systems/data design, domain depth, behavioral). Prepare for each round in order rather than only the first — the later stages usually carry the most weight.

How hard is the Databricks interview?

Databricks interviews are rated very high difficulty. The bar is highest on data engineering & distributed systems — go deep there and practise explaining your reasoning out loud.

What does Databricks look for in candidates?

Databricks focuses on Data engineering & distributed systems, Spark/lakehouse depth, coding. Culturally, it values Customer obsession, raise the bar, truth-seeking, ownership. Line up your examples to hit both the technical bar and these values.

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Compiled by PrepNPlaced from 30+ interview reports and question banks for the Databricks Qa Automation Engineer Sdet loop. Updated 2026.