seek Data Analyst Interview: Questions, Experience & Prep (2026)

Seek is a global employment marketplace connecting employers and job seekers across Australia, Southeast Asia, and India. Data drives every product decision at Seek, from how jobs get ranked in search results to how employers measure listing performance. This makes the Data Analyst role genuinely business-critical, not just a reporting function.

Seek currently has 70 open Data Analyst roles tracked across India. The interview process typically spans multiple rounds, candidates report, covering SQL and analytics reasoning, a case study or take-home assignment, and a business or behavioral panel. Interviewers focus on how well you reason about a two-sided marketplace: how employer behavior (posting, spending) and seeker behavior (searching, applying) interact, and which metrics tell the real story.

Salary bands for Data Analysts in India, based on the data available:

The sections below walk through the questions Seek interviewers typically ask, how to answer them, and what to avoid.

These questions reflect patterns candidates report from Seek Data Analyst interviews. They span SQL, product thinking, marketplace reasoning, and experience-based discussion.

1. Write a SQL query to find the top employers by job listing volume over a rolling window, using a window function to rank results.
2. How would you define a 'healthy marketplace' metric for a two-sided job platform like Seek?
3. A job listing is getting significantly fewer views than average for its category. Walk through how you would investigate the cause.
4. How would you analyze drop-off at each stage of the job application funnel, and how would you decide which stage to prioritize?
5. Describe a dashboard you built. Who used it, what decisions did it drive, and what would you improve today?
6. How would you segment job seekers on Seek's platform to personalize job recommendations?
7. Walk through how you would design an A/B test for a new job recommendation algorithm.
8. A product team brings you data from two sources showing different numbers for the same metric. How do you resolve it?
9. You have three stakeholders asking for analyses with overlapping deadlines. How do you decide what to work on first?
10. What metrics would you track to measure the success of a new feature that shows employers how many seekers viewed their listing?
11. How would you build an early-warning signal for employers who are likely to stop posting jobs on the platform?
12. Tell us about a time your analysis directly changed a business decision. What did you find, and what happened next?

Here are three STAR-format sample answers for questions that Seek interviewers typically ask.

Q: How would you analyze drop-off in the job application funnel?

*Situation:* At my previous company, we could see that many users who opened job listings did not complete applications, but we had no visibility into exactly where they were dropping off.

*Task:* I was asked to map the full funnel, identify the biggest drop-off points, and bring concrete recommendations to the product team.

*Action:* I pulled event-level data using SQL to track each step: listing view, 'Apply' click, form start, form completion, and submission. I calculated step-by-step conversion rates and segmented by device type, traffic source, and job category. I found that mobile users dropped off at the form-fill stage at a much higher rate than desktop users, concentrated in a specific job category.

*Result:* The product team used this to prioritize a mobile form redesign for that category. Candidates report that similar funnel analyses at Seek often feed directly into the product roadmap for the following cycle.

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Q: How do you handle conflicting data from two sources?

*Situation:* While preparing a monthly active users report, I found that our CRM and our analytics platform were showing different user counts for the same period.

*Task:* I needed to identify which number was correct, or reconcile them, before the report went to senior leadership.

*Action:* I traced the discrepancy to a definitional difference: the CRM counted any login as an active user, while the analytics platform counted only users who completed at least one meaningful action. I documented both definitions, aligned with the product owner on the right business definition, and rebuilt the report with a clear methodology note.

*Result:* The report was delivered on time with full transparency on how the number was calculated. Leadership adopted the stricter definition as the company standard going forward.

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Q: Tell us about a time your analysis changed a business decision.

*Situation:* My team was about to launch a paid add-on feature for small employers, based on the assumption that this segment was highly engaged with the platform.

*Task:* I was asked to validate that assumption using behavioral data before the feature went live.

*Action:* I analyzed employer activity data over 2024 and found that engagement was concentrated in a narrow sub-segment of small employers in specific industries. The broader small employer group showed very low return rates after their first posting, which contradicted the original assumption.

*Result:* The team narrowed the launch to the high-engagement sub-segment first. This reduced risk and gave them room to test pricing and messaging before committing to a wider rollout.

These frameworks help you structure answers clearly in any round of the Seek Data Analyst interview.

STAR (Situation, Task, Action, Result): Use this for all behavioral and experience-based questions. Keep Situation and Task brief, spend most of your time on Action, and always close with a specific Result. If you do not have a headline number to cite, describe the decision or process change the analysis enabled.

Funnel Thinking: For any question about user behavior on Seek, break the experience into stages (discovery, consideration, action, retention) and reason about conversion at each step. This maps directly to how Seek thinks about both job seekers and employers as separate user groups with different motivations and drop-off patterns.

Five Whys for Root Cause Questions: When asked why a metric dropped or a listing underperformed, do not jump to a solution. Walk through possible causes layer by layer: volume issue, quality issue, ranking or algorithm issue, external market factor. Show that you rule out hypotheses systematically before landing on a root cause.

Metric Definition Framework: When asked to define success for a feature, use three components: the primary metric (what directly measures the intended outcome), guardrail metrics (what you must not break), and leading indicators (early signals that move before the primary metric). This framework lands well at product-led companies like Seek.

Seek interviewers, candidates report, are looking for a combination of technical ability, marketplace intuition, and communication clarity. Here is how each dimension shows up.

Marketplace intuition: Seek is a two-sided platform. Interviewers want to see that you think about both employers and job seekers in every analysis. Candidates who reason only about one side of the marketplace tend to miss the interdependencies that drive Seek's core business metrics.

Communication over complexity: A sophisticated query that your stakeholder cannot act on is less valuable than a simple insight delivered clearly. Seek places weight on analysts who summarize findings in plain language and who proactively flag the limitations of their analysis, not just the headline number.

Ownership and initiative: Candidates report that Seek interviews probe for moments where you did not wait to be asked. Did you notice a data quality issue and escalate it? Did you add context to a report that was not requested but was clearly needed? These signals tend to matter more than candidates expect.

SQL and tool proficiency: Expect at least one hands-on SQL problem. Candidates report questions involving window functions, aggregations over time windows, and joins across multiple tables. Familiarity with a BI tool (Looker, Tableau, or similar) is expected for mid and senior roles.

A focused preparation plan based on what candidates report from Seek Data Analyst interviews.

Step 1: SQL and data fundamentals. Practice window functions (RANK, ROW_NUMBER, LAG, LEAD), multi-table joins, and aggregations on time-series data. Focus on queries that analyze funnel conversion, rolling windows, and user segmentation. These are the most commonly reported SQL topics from Seek interviews.

Step 2: Marketplace and product thinking. Study how two-sided marketplaces balance supply and demand. Practice defining success metrics for hypothetical Seek features using the primary metric, guardrail metric, and leading indicator framework. Prepare answers to 'how would you measure X' for at least five different product scenarios.

Step 3: Behavioral preparation. Prepare STAR answers for six to eight experiences from your past work: an analysis that changed a decision, a conflict between data sources you resolved, a time you managed competing stakeholder priorities, and a project you drove independently. Practice delivering each in under two minutes.

Step 4: Case practice and mock interviews. Do at least two full mock interviews with a peer or mentor. Practice the 'low listing views' investigation and the 'A/B test design' scenarios out loud. Time yourself on SQL problems to build comfort with thinking under observation.

If you are actively applying, knok checks 150+ job sites nightly, applies to jobs matching your resume, and messages HR for you, so your prep time goes toward interview skills rather than application tracking.

These are the most common mistakes candidates make in Seek Data Analyst interviews, based on patterns candidates report.

Jumping to solutions before defining the problem. When asked 'how would you investigate X', many candidates immediately propose a query or chart. Seek interviewers want to see you clarify the metric definition, the expected baseline, and the time window before discussing method. Pausing to scope the problem first signals analytical rigor.

Treating the interview as a SQL test only. SQL is one component, not the whole interview. Candidates who focus heavily on technical prep and under-prepare on business judgment, stakeholder communication, and case reasoning tend to struggle in later rounds.

Vague STAR answers. Answers like 'I improved a dashboard and the team found it useful' do not land. Ground your results in specific decisions made, actions stakeholders took, or process changes that followed, even if you cannot cite a headline number.

Ignoring both sides of the marketplace. Any analysis question at Seek has an employer angle and a seeker angle. Candidates who reason only from one side tend to lose credit even when their technical logic is correct.

Not asking clarifying questions in case studies. Candidates report that Seek interviewers expect at least one or two clarifying questions before you begin working through a case. Asking nothing signals overconfidence or a lack of analytical rigor.