What Experience Do You Have Acting as a Liaison Between Data Engineering and Business Departments?

This question gets asked a lot for senior data engineering roles, and it often trips people up because they try to answer it purely technically. The interviewer isn’t asking about your SQL skills here. They want to know if you can function as the person who keeps both sides of the table productive — the engineers who build the systems and the analysts, product managers, or executives who depend on the data those systems produce.

Why This Question Gets Asked

In 2025, data engineering teams are increasingly expected to work in direct partnership with business units. Gone are the days when a data engineer could hand off a schema and call the job done. With the rise of data mesh architectures, embedded data teams, and self-serve analytics platforms, the ability to understand business requirements, translate them into technical decisions, and communicate back clearly has become a core part of the role.

STAR Answer Example

Situation

I was a senior data engineer at a healthcare SaaS company, and our team supported four different product lines — claims processing, provider directories, patient engagement, and compliance reporting. Each of those areas had their own analytics needs, their own vocabulary, and their own definition of what “good data” looked like. The data engineering team was centralized but served all four groups.

The problem was that each group had developed a habit of raising ad-hoc requests without context — a ticket would come in saying “we need this field added to the report” with no explanation of the downstream business question being answered. This led to engineers building things that didn’t actually solve the problem, which created rework and eroded trust on both sides.

Task

After a particularly frustrating quarter where two completed pipeline updates had to be rebuilt from scratch because the requirements were misunderstood, my engineering lead asked me to improve how the team gathered and managed requirements. I took that on as a project alongside my regular engineering work.

Action

The first thing I did was go talk to the stakeholders directly, which sounds obvious but hadn’t been happening systematically. I set up 30-minute discovery calls with the analytics lead from each of the four business areas. I came with a simple set of questions: What decision are you trying to make with this data? What does the right answer look like when you see it? How often does that decision change, and does the data need to be fresh to the minute or is daily enough?

Those conversations revealed a lot. The compliance team, for example, had been requesting daily refreshes on a regulatory reporting table, but when I dug in, the actual compliance submissions only went out monthly. A weekly refresh was perfectly adequate. That single conversation removed a job from our pipeline that was running seven times more often than needed.

I then worked with the team to create a lightweight requirements document we called a “data brief” — essentially a one-page template that any stakeholder needed to fill out before a ticket moved into the engineering queue. It asked for the business question, the intended users, the expected refresh frequency, any known data quality concerns, and what “done” looked like for them. The brief wasn’t meant to be bureaucratic. Most could be completed in 15 minutes, and I offered to write the first draft on behalf of stakeholders who preferred to talk through it verbally.

I also set up a monthly cross-team review where I walked each business area through any pipeline changes we’d made, any data quality incidents and how we’d resolved them, and what was coming up in the next sprint that might affect them. This replaced the pattern of stakeholders discovering changes by surprise.

For ongoing issues, I became the first point of contact for data-related questions or discrepancies. Rather than having a business analyst email the entire engineering team and wait days for a response, they’d come to me, I’d do initial triage, and I’d either resolve it directly or bring in the right engineer with a clear problem statement already prepared.

Result

Over the following two quarters, the rate of rework on completed pipeline work dropped significantly — from roughly 4 instances per quarter to 1. The monthly reviews started drawing in people from teams who hadn’t been attending at all previously, including a product manager from the patient engagement team who started bringing her roadmap to our meetings so we could flag any data impacts early. The compliance team’s unnecessary daily refresh was removed, saving us compute time and simplifying the pipeline dependencies. My manager mentioned in my performance review that stakeholder satisfaction with the data team had “noticeably improved” based on feedback she’d received from the business leads.

What Makes This Kind of Work Effective

A few patterns emerged from that experience that I’d apply anywhere:

Lead with the business question, not the technical ask. A request for “add column X” is never really about column X. Asking “what decision does that column help you make?” almost always reveals the actual need, which is sometimes simpler and sometimes more complex than the original request.

Create lightweight process, not heavy process. A one-page brief is sustainable. A six-page requirements document is not. People will skip it or fill it in poorly just to get past the gate.

Regular touchpoints prevent surprises. The monthly review felt like overhead to plan, but it consistently caught potential misalignments before they became incidents.

Triage is a skill. Being the person who figures out whether something is a data quality issue, a pipeline bug, or a misunderstood metric takes domain knowledge and communication ability. It’s genuinely valuable and often goes unrecognized.

Key Takeaway for Your Interview

When you answer this question, show that you’ve done the work in both directions — not just translating technical concepts into business language, but also translating business requirements into precise technical specifications. That bidirectional fluency is what makes a data engineer genuinely effective as a liaison, and it’s what the interviewer is listening for.