What Hiring Managers Actually Look For in Data Engineers

The Real Story Behind Data Engineering Hiring

The job market for data engineers is brutal right now; layoffs, hiring freezes, and 200+ applicants for every decent role. But some candidates still get offers within weeks. What’s the difference?

It’s not always the fanciest credentials or the longest list of technologies. It’s something more fundamental, and more learnable.

What Actually Matters (And What Doesn’t)

The Skills That Don’t Impress Anymore

Every data engineer resume lists Python, SQL, Spark, AWS/Azure/GCP, Airflow, Kafka, and dbt. Most mention “experience with big data” somewhere. These are table stakes; if you don’t have them, you won’t get an interview. But having them doesn’t make you stand out.

Same with certifications. It proves you can pass a test, but it doesn’t prove you can build reliable data pipelines under pressure.

What Actually Gets Attention

Hiring managers stop scrolling and pay attention when they see specific impact with numbers. Not “built data pipelines” but “reduced pipeline runtime from 6 hours to 45 minutes, saving $12K monthly in compute costs.” This demonstrates understanding of business impact, not just technical execution.

Production experience with failure stories matters more than perfect success records. Anyone can build something that works once. Hiring managers want to know what broke, how it was fixed, and what was learned. The best engineers can articulate their biggest production failures and what they did differently afterward.

End-to-end ownership separates candidates too. Did you just write code someone told you to write? Or did you identify a business problem, propose a solution, build it, deploy it, and monitor it in production? That’s a big difference.

The Five Things Hiring Managers Are Actually Evaluating

When evaluating data engineers, hiring managers look for five specific things. Master these and you’ll get offers.

1. Can You Think Like a Product Engineer?

Data engineering isn’t just moving data from point A to point B. It’s building products that other people use; people like data analysts, data scientists, and business stakeholders. That means asking about the downstream use case before designing the pipeline, considering data quality as a first-class concern rather than an afterthought, thinking about documentation and discoverability, and building monitoring that identifies issues before users complain.

The interview question that reveals this: “Walk me through a data pipeline you built. Who were the users? How did you know it was successful?” If you can’t answer the “who” and “why,” that’s a red flag. Technical chops don’t matter if you’re building the wrong thing.

2. How Do You Handle Ambiguity?

Requirements are always vague, because stakeholders rarely know what they want. Source systems change without warning. Data quality is terrible. Great data engineers don’t wait for perfect requirements. They make reasonable assumptions, document them, build something, get feedback, and iterate.

Hiring managers listen for phrases like “I talked to three analysts to understand their workflow” or “The requirements were unclear, so I built a quick prototype to validate assumptions” or “I documented my assumptions and got sign-off before building the full solution.” These show initiative and pragmatism.

Red flags include “I just built what they asked for” or complaining that “requirements kept changing” as if that’s unusual. The job is ambiguous. If you need hand-holding, you won’t succeed.

3. Can You Debug Like a Detective?

Production systems break. Pipelines fail at 3am. The question isn’t whether you can prevent problems, it’s how fast you can diagnose and fix them.

Great debugging means starting with symptoms, forming hypotheses, and testing them systematically. It means checking logs, then metrics, then data quality in that order. It means documenting the root cause and the fix, then adding monitoring to prevent recurrence.

Many interviews include a broken pipeline scenario to watch how candidates think through it. The best candidates ask clarifying questions, narrow down possibilities, and explain their reasoning. The worst candidates immediately jump to solutions without understanding the problem. That’s how you waste three days chasing the wrong issue.

4. Do You Write Code Other People Can Maintain?

Your code outlives your employment. When you leave, someone else needs to understand and maintain your work. That means clear naming conventions instead of variables like x, temp, or “df_final_v2_really_final.” It means functions that do one thing well, comments that explain why rather than just what, error handling that provides useful messages, and tests for critical logic.

Hiring managers don’t care if you can write clever one-liners. They care if the next engineer can understand your code at 2am when it breaks. Many interviews include a code review exercise that shows you messy code and asks you to improve it. This reveals whether you understand good practices or just follow them when forced.

5. How Do You Prioritize When Everything Is Urgent?

You’ll always have more work than time. Five stakeholders want their pipelines built yesterday. Technical debt is piling up. Which fires do you fight?

Strong candidates ask about business impact and urgency, negotiate timelines based on effort and value, communicate trade-offs clearly, and say no to low-value work. Weak candidates say “I just do whatever my manager tells me,” can’t estimate work, take on everything and deliver nothing, or never push back on unrealistic timelines.

Senior engineers protect their time and their team’s time. Junior engineers say yes to everything and burn out.

The Take-Home Project

If you make it past phone screens, you’ll probably get a take-home project. Here’s what gets evaluated:

Good projects include a clear README with setup instructions, reasonable assumptions documented, code structure that makes sense, basic error handling, some level of testing, and consideration for production deployment. Projects that really impress include Docker setup for easy running, data quality checks, monitoring and observability considerations, trade-offs documented in comments or README, and code that works on the first try.

Instant rejection signals? No README, code that doesn’t run without debugging, no error handling whatsoever, solutions that are overly complex for the problem, or copy-pasted code with no understanding. Hiring managers would rather see a simple solution that works reliably than a complex solution that barely runs.

Questions You Should Ask

The interview goes both ways. Questions about the team show engagement: “What does a typical sprint look like?” or “How do you handle on-call for data pipelines?” or “What’s the biggest data quality challenge right now?” Questions about the technical stack demonstrate depth: “What’s your data infrastructure like?” or “How do you handle schema evolution?” or “What’s your approach to testing data pipelines?” Questions about growth show ambition: “What does career progression look like for data engineers here?” or “What would success look like in my first 90 days?” or “What’s the biggest technical challenge I’d be working on?”

Questions that worry hiring managers? Only asking about benefits and PTO, no questions about the technical environment, no curiosity about the business domain, or immediately asking “How quickly can I get promoted?”

Red Flags That Kill Applications

Resume red flags include listing technologies you’ve barely used, showing no concrete achievements or impact, job hopping every six months, spelling errors or sloppy formatting, and generic objective statements.

Interview red flags include badmouthing previous employers, inability to explain your own projects, no questions about the role, arrogance or dismissiveness, and blaming others for failures.

Technical red flags include inability to write a basic SQL query, no understanding of data quality, never having deployed anything to production, no experience with version control, and zero testing or monitoring experience.

The Bottom Line

The data engineers who get hired aren’t necessarily the ones with the most impressive resumes; they’re the ones who understand the business impact of their work, handle ambiguity without hand-holding, debug systematically and document their findings, write maintainable code, prioritize ruthlessly, communicate clearly with technical and non-technical folks, take ownership of outcomes, and show genuine curiosity about the domain.

Technical skills matter, but they’re the baseline. The differentiator is how you think about problems, how you work with others, and whether you deliver reliable solutions.