LinkedIn

Situation & Behavioral

💬 Situation & Behavioral 14 guides · updated 2026

The non-technical rounds that still decide offers — STAR-structured answers for leadership, conflict, and ownership questions in modern tech interviews.

What Aspects of Your Previous Job Are Most Relevant to This Position?

This question sounds simple but it’s actually a targeted one. Interviewers want to know whether your past experience maps cleanly onto what they need, and whether you understand the role well enough to make that connection yourself. The answer isn’t just a resume recitation — it’s a deliberate translation of your background into their context.

Why This Question Gets Asked

In 2025, data engineering roles have broadened considerably. Teams expect engineers to be comfortable with cloud-native tooling, data lakehouse architectures, streaming systems, and stakeholder collaboration — all at once. When you’re asked what’s relevant from your past, the interviewer is checking whether your experience covers those bases and whether you’re self-aware enough to articulate how.

STAR Answer Example

Situation

In my previous role at a mid-size retail analytics company, I worked as a data engineer on a team of six. We were responsible for the full data lifecycle — ingesting raw transaction data from dozens of point-of-sale systems, transforming it through a dbt and Spark-based pipeline, and delivering it into a Snowflake data warehouse used by analysts and an external-facing BI product. The business had grown quickly through acquisitions, so we were constantly dealing with heterogeneous data sources and tight turnaround expectations from new stakeholders who weren’t familiar with how data pipelines worked.

Task

My role covered pipeline development, quality monitoring, and a lot of stakeholder-facing work to help the analytics team understand what was available and what the data meant. I had to be both technically strong and able to communicate clearly in non-technical terms.

Action

There were a few areas where I built particularly deep experience.

The first was building and maintaining robust data pipelines at scale. I owned several core ingestion pipelines that processed upwards of 50 million rows per day. I built them with failure recovery in mind from the start — using Airflow with task-level retries, dead-letter queues for malformed records, and alerting through PagerDuty when row counts deviated from expected ranges. That discipline of building reliable-by-default pipelines is something I carry into every role.

The second was hands-on work with cloud platforms. We migrated our primary warehouse from Redshift to Snowflake midway through my tenure, and I was part of the small working group that handled the migration planning, data validation, and the gradual cutover process. I learned a lot about data contract management and what happens when you shift consumers to a new schema — communication and phased rollouts matter enormously there. That experience maps directly onto cloud-heavy roles.

The third area was cross-functional collaboration. Our analytics team included both technical and non-technical members, and a big part of my job was translating between what the pipeline produced and what the business actually needed. I ran monthly data review sessions where I walked analysts through any schema changes, new datasets available, or known data quality issues. Those conversations directly shaped our pipeline roadmap. I got comfortable being the person who bridges that gap.

Result

By the time I left, the pipelines I had owned had zero unplanned outages over an 18-month stretch. Our data freshness SLA, which had previously been breached around once a month, was consistently met. The data review sessions I started became a formal part of the team’s quarterly process. And when I transitioned out, the knowledge documentation I’d built meant the team could hand off my responsibilities in two weeks rather than two months.

Connecting Past to Present

When you’re preparing to answer this question, do two things before you walk into the room.

First, reread the job description carefully and underline every technical and interpersonal skill they ask for. Note which ones you have clear experience with and which ones you’ve touched but aren’t as strong in.

Second, for every skill they list, find one concrete story from your past that demonstrates it. Not a vague claim — a specific project, problem, or outcome.

The answer that lands is the one that says: “Here’s what you need. Here’s where I’ve already done it. Here’s what happened when I did.”

What Translates Best in 2025-2026 Roles

Interviewers for data engineering positions are currently paying close attention to:

If your past role touched even three or four of these, make sure you name them explicitly and give a short example for each. The goal is to make the connection obvious enough that the interviewer doesn’t have to work to see it.