Week 28/31: Behavioural Interview Questions for Data Engineers

How to prepare, structure answers, and impress interviewers with real word examples.

Aug 17, 2025

So far in our data engineering interview preparation series1, we’ve focused heavily on the technical skills that are essential for data engineering and similar roles:

This week, we’re shifting focus to an equally important, but often overlooked, area: behavioural interview questions.

From our experience, behavioural questions can show up at any stage of the interview process, from an initial phone screen to later rounds or sometimes not at all. But when they do, they can make a big difference.

Why? Because they aren’t designed to test our technical abilities only. Instead, they assess areas like:

Motivation and initiative
Communication and collaboration
Problem-solving and adaptability
Ownership, proactivity and growth mindset

In other words, they reveal how we work with people and handle challenges, not just how well we write SQL or design pipelines.

Remember: Every interview question is a chance to showcase our strengths, demonstrate our influence, and prove that we’re more than just technically competent.

Therefore, the more we organise our thoughts and practice our answers ahead of time, the more confidently we’ll be able to share the right stories at the right moments.

In this post, we’ll share what we’ve learned through both experience and research, including:

How to give strong, clear answers in interviews?
3 Rules for better tailoring our responses to the job description.
What are behavioural questions?
How to prepare for behavioural questions using:
- SRAR method
- 6-step process with a real practical example.
Sample behavioural questions from tech companies like Amazon, Netflix, Airbnb and many more.

While I’ll include data engineering-specific examples, the lessons here are universal and can benefit anyone preparing for interviews.

🎉We’re excited to announce that
Yordan Ivanov
, Head of Data Engineering at Dext, with 15 years in software and 8 years building modern data platforms, will join us to share his valuable insights on data engineering interviews.🎉
He has conducted hundreds of interviews with software, data, and analytics engineers and we beleive his experince would be really valuable for us.
Have questions for Yordan? Please leave them in the comments, and we’ll ask on your behalf. Stay tuned, his insights will be published in the coming weeks.

Want to learn more from Yordan? Check out his Substack2 and connect with him on LinkedIn3.

How To Give Strong, Clear Answers In Interviews?

Before we delve into behavioural questions and how to address them, its important to step back and understand what makes a strong interview answer in the first place?

1. Detailed and specific

Strong answers use numbers, statistics, and concrete examples to back up our story. They avoid unclear statements like:

“I’m a hard worker, so I’m sure I’d do well in this job.”

Instead, they provide evidence, measurable results or specific actions that prove our point.

2. Direct and clear

Interviewers value clarity. A great answer:

Addresses the exact question asked.
Stays on-topic without drifting into irrelevant details.
Delivers just enough context to be convincing, without overexplaining.

Bad example
"Well, in my previous role, we had a lot of projects going on, and sometimes priorities were shifting. Once, a data pipeline broke, and it was challenging because multiple stakeholders were involved, and we had to determine how to resolve the issue. I talked to some people, and eventually, we found a solution, but it took a while because there were different opinions about the best approach."

Good example
"In my last role, one of our critical ETL pipelines failed just before a reporting deadline. I quickly diagnosed the issue, identified that a schema change caused the failure, and updated the pipeline to handle the new format. I also tracked the problem to implement quality checks after that. As a result, we restored the pipeline within two hours, and the reporting team delivered on time. I also implemented the quality checks to prevent these kinds of issues”.

3. Results-oriented

Rather than focusing on what we could do, great answers highlight what we’ve already accomplished. Past achievements are the best predictor of future success, so prepare real success stories that align closely with the role we’re applying for.

Bad example (future-focused):
"I’m confident I could improve your data pipeline because I’m a hard worker and I always try to make processes more efficient."

Good example (past achievement with measurable outcome):
"In my previous role, I optimised a nightly ETL job that was taking over 8 hours to run. By redesigning the workflow and adding partitioning, I reduced the runtime to just under 2 hours. This freed up server resources and allowed the analytics team to access fresh data earlier in the day; improving their reporting accuracy."

4. Tailored to the company

Most candidates focus on themselves, but the best candidates make it about the employer. Show we understand:

Their challenges and goals.
How do we solve their problems?
How can we help them make or save money?

Our examples should clearly connect our past experience to their future needs.

Bad example (self-focused):
"I’ve worked on a lot of data modelling projects in the past, and I’m confident I can apply those skills here. I really enjoy creating data models and always keep up with the latest techniques."

Good example (employer-focused):
"From your job description, I see you’re working on building a unified customer data platform. In my last role, I redesigned the product sales model to align 12 different source systems into a single star schema. This reduced query times by 60% and gave the marketing team a trusted single source of truth for campaign performance. I’d love to bring that experience here to help your team build scalable, analytics-ready models that drive faster insights and more accurate reporting."

3 Rules for Tailoring Our Responses to the Job Description

In interviews, “tailoring” means customising our answers to fit the specific company, team, and role we’re applying for

Is it really necessary?

Absolutely. We’re competing with other candidates; tailoring can be the factor that wins us the offer and potentially a better role.

Here’s the three-step method we recommend for perfect tailoring:

Rule 1: Know our audience

Before the interview, research the company and role thoroughly:

Read the job description carefully.
Visit the company website and LinkedIn page.
Understand how they make money, who their customers are, and what this role does.
Learn about the person or people you’ll be interviewing with.

We can’t give targeted answers if we don’t know what matters to them.

Rule 2: Think from their perspective

Once we understand the business and the people, we step into their shoes.
Ask ourselves:

What problems are they trying to solve?
What skills or experiences do they value most?
Which requirements are highlighted first in the job description?
Based on their roles, what might they ask about or be most interested in hearing?

The better we think like a hiring manager, the better we can position ourselves as close to who they need.

Rule 3: Make our answers about them

With their priorities in mind, prepare specific, results-driven stories that:

Highlight our relevant skills.
Show how we’ve solved similar problems before.
Connect our experience directly to their goals and challenges.

Even when we’re talking about our achievements, keep the focus on how it benefits them. We’re not just telling our story, we’re showing them a preview of what we’ll do for their team.

What Are Behavioural Questions?

Behavioural interview questions are designed to see how we’ve handled situations usually in the past. Past behaviour is usually the most reliable indicator of how someone will perform in the future.

They usually ask us to describe a specific situation or experience and often start with phrases like:

“Tell me about a time you ___”
“Give me an example of a situation where you had to ___”

These questions can feel overwhelming, but the good news is: there’s a simple, repeatable method (like the STAR method) to answer them effectively.

Behavioural questions help interviewers assess:

Cultural fit: Do our values align with the company’s?
Teamwork: How well do we work with others?
Motivation and values: What drives us to do our best work?
Adaptability and problem-solving: Can we adjust when things change?
Time management: How do we handle competing priorities?
Communication skills: Can we clearly explain our thoughts and decisions?
Growth mindset: Are we open to learning, feedback, and continuous improvement?

What is the STAR method?

The S.T.A.R. method is a simple framework for structuring clear, impactful answers to behavioural questions. It ensures we provide enough detail without going off-topic, and that we highlight both what we did and the results we achieved.

We generally try to use this method to answer any type of question whenever possible, because it helps clearly convey the message we want to deliver.

Let’s explain it with an interview question example:

Imagine the interviewer asks this question:

Tell me about a time when you faced recurring issues with a data pipeline and how you addressed them to ensure data quality and reliability?

How would we answer this using the STAR method?

Situation (S)
Briefly describe the context and challenge we faced. Set the scene so the interviewer understands the background.

As a Data Engineer at X company, I was responsible for maintaining a monthly pipeline that extracted data from multiple sources. The pipeline took about 6 hours to run and produced four key metrics for stakeholders. However, I frequently faced issues such as missing or outdated sources, schema changes breaking the pipeline, or other quality problems. These failures often required expensive reruns on EMR clusters and delayed downstream teams ( sometimes 2-3 days)

Task (T)
Explain our specific responsibility, goal, or the problem we needed to solve.

My responsibility was to ensure the pipeline ran smoothly and delivered accurate results on time, while also reducing costs from reruns and improving reliability for stakeholders.

Action (A)
Walk through the specific steps we took and why we chose those actions. Be clear about our personal contribution.

I started by documenting every failure in an issue tracker to identify recurring problems. Based on these patterns, I designed a data quality framework aligned with quality metrics. I then implemented automated tests at three stages:

Before the pipeline (schema validation, nnumber of rows, data freshness checks).
During the pipeline (row count monitoring, anomaly detection).
After the pipeline (metric validation before sharing with stakeholders).

Result (R)
Share the outcome of our actions. Where possible, include measurable results and lessons learned.

By implementing this framework, I reduced unexpected failures and reruns, saving significant EMR costs which costs 100 US dollor per hour. Wee could detect any issues earlier and take actions accordingly( somtimes it could take 2-3 days to slove it). Stakeholders received reliable metrics on time, and our team spent less time firefighting. This also improved trust in the data and allowed me to apply the same approach to other pipelines in the company.

How to Prepare for Behavioural Interviews?

Preparing for behavioural questions is about aligning our past experiences with the role’s requirements and being able to communicate them clearly. Here’s a step-by-step approach, followed by a real-world example.

1. Review the job description

Carefully read the job posting and highlight keywords related to behavioural traits, for example, “time management”, “collaboration”or “problem-solving”.
If the job description emphasises a skill multiple times, we’ll likely be asked about it in the interview.

Note: While AI tools can help summarise or highlight keywords, we recommend reading the full job description yourself at least once to fully understand the role and context.

2. Identify relevant questions

Look at common behavioural question examples, especially ones that match the keywords we found in the job description.
This helps us anticipate the types of scenarios we might be asked to discuss.

3. Recall our scenarios

Think of real experiences from our careers that align with those behavioural traits.
For example, focus on situations where we:

Overcame challenges.
Worked with a team.
Solved a problem under pressure.
Managed competing priorities.

Note: If our previous roles don’t match exactly, use similar experiences. Graduates can use university projects, internships, or even casual work experiences.

4. Structure our answers with the STAR method

Using the STAR Method we discussed earlier to write down each story so it’s structured and easy to follow.

Be specific, concise, and results-oriented.

5. Practice out loud

Rehearse our answers aloud, and record ourselves if possible.
Listen for:

Clarity: Did we follow the STAR structure?
Confidence: Does our delivery sound assured?
Conciseness: Are we avoiding unnecessary details?

6. Prepare for follow-up questions

Interviewers often dig deeper. Be ready to:

Explain why we took certain actions.
Share what we learned from the experience.
Discuss how we’d handle a similar situation today.

The goal isn’t to memorise answers word-for-word, but to be so familiar with our stories that we can adapt them naturally to any behavioural question.

Applying the Process: Real Job Example

Let’s apply this to a real job posting from SEEK4, a common platform in Australia:
Data Engineer – Accenture5

Steps 1 and 2:

After reviewing the job description, we highlighted keywords and sections relevant to behavioural questions below and mapped them to potential questions:

1. Communication and Stakeholder management

“Ability to understand and articulate requirements to technical and non-technical audiences”
“Stakeholder management and communication skills, including prioritising, problem solving and interpersonal relationship building”

Possible questions:

Tell me about a time you had to explain a technical concept to a non-technical stakeholder.
Give an example of when you managed conflicting priorities among stakeholders. How did you handle it?

2. Teamwork and collaboration

“You will be working on client projects with teams from across our Modern Data Platform and Applied Intelligence practice alongside our industry, functional and technology SMEs”
“You enjoy working both freely and as part of a team…”

Possible questions:

Describe a time when you collaborated with cross-functional teams on a challenging project.
Tell me about a time you had to influence or gain buy-in from colleagues with different expertise.

3. Adaptability and problem-solving

“Work in a fast-paced complex environment with conflicting priorities”
“Naturally inquisitive and open to exploration of underlying data, finding valuable insights”

Possible questions:

Give me an example of when you had to work under tight deadlines with shifting priorities.
Tell me about a time you had to dig deep into data to uncover insights or solve a problem.

4. Ownership and delivery

“You will be involved in all aspects of data engineering from delivery planning, estimating and analysis, all the way through to… production implementation”
“Experience delivering in an agile environment”

Possible questions:

Tell me about a time when you owned a project end-to-end. What challenges did you face, and how did you deliver?
Share an example of a project where Agile practices helped you succeed (or where you had to adapt Agile to make it work).

5. Innovation and initiative

“We believe that delivering value requires innovation”
“Opportunities to keep skills relevant through certifications, learning, and diverse work experiences”

Possible questions:

Tell me about a time you introduced a new idea, tool, or process that improved a project or workflow.
Give me an example of how you kept your skills up to date and applied them in your work.

Next Steps

Now it’s time to prepare our answers by:

Recalling relevant scenarios from our experience.
Structuring them with STAR.
Practising out loud.
Preparing for follow-up questions.

We leave it with you so good luck !🙂.

Following this approach will help us feel confident, organised, and ready to showcase our skills in every interview scenario.

Sample Interview questions

Now that we’ve covered how to prepare and answer behavioural questions, let’s look at real examples from tech companies.

Amazon: Tell us about your greatest achievement.6
Google: Tell me about a time you brought disagreeing teams together.7
Netflix: What are some effective ways to make data more accessible to non-technical people?8
Airbnb: Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?9
Microsoft: How do you prioritise multiple deadlines? Additionally, how do you stay organised when you have multiple deadlines?10
Stripe: Share an example of a time you had to learn a tool quickly under pressure.11
Xero: Tell me about a time you went above and beyond at work. What did you do, and why did you do it?

Conclusion

Behavioural interviews are an opportunity to show more than technical skills, they reveal how we solve problems, collaborate, and deliver results. By understanding what interviewers are looking for, preparing relevant stories from our experience, and structuring answers with the STAR method, we can confidently communicate our impact. Practising ahead, tailoring responses to the company, and reflecting on lessons learned ensures we present ourselves as capable, adaptable, and ready to contribute from day one.

If you want to learn more about data engineering interviews, we recommend checking out these posts by

Yordan Ivanov

Interested in learning more about data engineering?

Check out our Data Engineering Lifecycle series here.

Looking to become a more successful data engineer? Explore our tips and insights to level up your skills and career.