Hire Data Engineer took us four months the first time. Four months, eleven interviews, two exploded offers, and one very expensive lesson about what happens when you don’t know what you’re looking for before you start looking.
That’s not a hypothetical. That’s a real thing that happens to real companies — often companies that are otherwise pretty good at hiring. The problem isn’t effort. Most hiring managers trying to fill this role are working hard. The problem is usually that they’re working hard on the wrong things.
So this is not going to be another article full of generic advice you’ve already heard. We’re going to get specific about what actually works, where companies tend to get stuck, and how to move faster without lowering your standards.
First, Get Clear on What You’re Actually Buying
One of the most common things I see is a company that thinks they need a data engineer but actually needs something else entirely. Or they need a data engineer but have described the job in a way that will attract the wrong person.
Data engineers are infrastructure people. Their job is building and maintaining the systems that move data around — pipelines that pull from your databases, APIs, and third-party tools, clean that data up, and push it somewhere your analysts can use it. They’re the ones making sure Snowflake has the right tables, that the Airflow DAGs run on schedule, that when someone on the analytics team says “the numbers look weird,” there’s actually a process for figuring out why.
They are not the people building dashboards. That’s more of an analyst job. They’re not training machine learning models either — that’s data science. And they’re not your software engineers, even though they write code. These are related disciplines but genuinely different ones, and if you write a job description that blurs those lines, you’ll waste weeks sorting through applications from people who aren’t right for the role.
Get really specific about what the person you’re hiring will actually own. What pipelines need to be built? What’s currently broken? What does your stack look like — are you on AWS or GCP or Azure, are you using dbt, what’s your warehouse? The more concrete you can get about the actual work, the better everything downstream goes — the job description, the sourcing, the interviews, the offer.
The Market Is Difficult and Pretending Otherwise Won’t Help You
Here’s something a lot of job postings don’t acknowledge but candidates absolutely know: experienced data engineers have options. Plural. Good ones usually aren’t unemployed and scrolling job boards. They’re at a company, doing okay, and maybe passively open to something better — but “maybe passively open” is very different from “actively applying to your opening.”
This matters because it changes how you need to run your search.
If you just post a job and wait, you’ll get applications. But the candidates who apply through cold job postings tend to be people who are actively looking, and there are real reasons people are actively looking — sometimes good reasons, sometimes not. You’re not necessarily getting the best pool, you’re getting the available pool.
The people you actually want — three to seven years of real experience, have built and run production pipelines, know how to work with stakeholders and not just code — those people need to be reached, not waited for.
That’s not impossible. It just means being more active about sourcing. Your own employees are usually the best starting point. Data engineers know other data engineers, and a referral from someone on your team carries more weight than a cold message from a recruiter. Beyond that, LinkedIn outreach works if you actually personalize it — not “I came across your profile and think you’d be a great fit” copy-paste stuff, but something that shows you actually looked at what they’ve worked on and have a specific reason for reaching out.
And for a lot of companies, especially ones that don’t have a strong employer brand in the tech community, a staffing partner with actual relationships in this space can be worth the cost just in terms of getting you in front of candidates who wouldn’t have found you otherwise.
Writing a Job Description That Doesn’t Immediately Turn People Off
Most data engineering job descriptions are bad. I don’t mean that harshly — I just mean they don’t work well. They’re usually a list of requirements with no real information about what the job is actually like, written in a way that suggests the company wants someone who can do everything, for a salary range that gets revealed only after multiple rounds of interviewing.
Strong candidates — the ones who have choices — read job descriptions and make a call in about forty-five seconds. They’re asking: does this seem real? Does this seem interesting? Does this company seem to know what they want? If the answer to any of those is no, they move on.
So make the job description actually useful. Describe the specific problem this person is being hired to solve. Mention the actual tools in your stack — not “experience with cloud platforms” but “we’re running on AWS, using Redshift and dbt, and moving toward Kafka for streaming.” Include something real about what the team is like, how decisions get made, what the culture around data is. And put the salary range in the posting. Candidates are going to ask about compensation eventually, hiding it just creates friction and tells them something about how you negotiate.
You’re not just filtering candidates with a job description. You’re selling them on the opportunity. Treat it that way.
Screening: What You’re Actually Looking For Early On
The first conversation with a candidate — usually thirty or forty minutes with a recruiter or hiring manager — is not the place for technical depth. You’re trying to answer a few basic questions: Does this person have relevant experience? Can they communicate clearly? Are they actually interested in this role or just casting a wide net?
Ask them to walk you through something they built recently. Not a general description — a specific project. What was the source, what was the destination, what were the hard parts, what would they do differently? You’ll learn a lot from how they answer that question. Someone who knows their work well can talk about it in detail. Someone who’s exaggerating their resume tends to get vague when you push on specifics.
Also just notice how they communicate. Data engineers work across a lot of different teams — analytics, product, engineering, sometimes executive stakeholders. Someone who can’t explain what they do without drowning you in jargon is going to struggle in that environment. You don’t need them to dumb it down, you just need them to be clear.
If the first call goes well, you move to technical evaluation. This is where a lot of companies go sideways.
Technical Interviews: Stop Using Puzzles That Have Nothing to Do With the Job
I’ve talked to a lot of data engineers over the years and almost all of them have a story about a terrible technical interview. The most common complaint: being asked to solve abstract coding problems that have nothing to do with data engineering. Graph traversal algorithms. Linked list manipulations. Stuff that shows up in software engineering interviews at big tech companies but is genuinely irrelevant to the work of building and maintaining data pipelines.
Why does this happen? Usually because the company borrowed their interview process from their software engineering team without thinking about whether it applied. It doesn’t.
A good technical interview for a data engineer looks like actual data engineering work. Give them a schema and ask them to write the SQL to answer a real-ish business question. Show them a pipeline design and ask what could go wrong. Give them a sample of messy data and ask how they’d think about cleaning and modeling it. Ask them to talk through how they’d design an ingestion process for a specific type of data source. These things are bounded, relevant, and tell you something real about how they think.
Keep it to two or three hours total, including any take-home component. If you’re asking for more than that, compensate them. Asking someone to do six hours of unpaid work as a condition of interviewing is a bad look and strong candidates will walk.
Final Rounds: You’re Not Still Testing, You’re Deciding
By the time someone gets to a final round, you should already know they can do the job technically. The final round is about everything else — do you actually want to work with this person, do they seem genuinely interested in this specific role, are there any concerns that didn’t come up earlier?
Have them meet the people they’d work with day to day. Not just their direct manager, but the analysts who’d be depending on their pipelines, the engineers they’d be coordinating with. Those people will pick up on things you won’t, and it gives the candidate a real sense of the team.
Ask about decisions they’ve made in past jobs — particularly hard calls. How they handled a project that went sideways. How they dealt with a stakeholder who wanted something they knew was the wrong approach. The answers tell you a lot about how they operate when things aren’t going smoothly, which is when you actually need to know what someone’s made of.
And leave real space for their questions. A candidate who comes to a final round with good, specific questions is someone who’s been paying attention and is actually interested. Someone who asks nothing — or only asks about the health insurance — usually isn’t that excited, and you should probably find out why before you make an offer.
Compensation: The Thing That Kills More Searches Than Anything Else
Mid-level data engineers — three to five years of real experience — are typically going to cost you somewhere north of $110,000 in most US markets. Senior engineers with broader experience or specialised skills push well above that. Staff and principal-level people at larger companies can be significantly higher.
If those numbers feel like a lot, I understand. But here’s the reality: trying to hire at significantly below market doesn’t save you money. It wastes your time. You’ll spend eight weeks on a search, get to offer stage, and watch the candidate accept something else for $25,000 more. You’ll do that two or three times before you either raise the budget or hire someone who isn’t quite right. The months you lost have a real cost too.
Know your range before you start. Be honest about it early in the process — not necessarily in the first five minutes, but before the technical round. A candidate who goes through four interviews only to get an offer that was never going to work is going to be annoyed, and annoyed candidates talk to other candidates.
Speed Matters More Than Most Companies Realize
Here’s a dynamic that plays out constantly in this market: a company finds a strong candidate, does a good first round, then takes ten days to schedule the technical interview because the hiring manager is travelling. By the time they get back to the candidate, there’s an offer already on the table from somewhere else.
Data engineers in high demand — which is most of the good ones — move through multiple processes simultaneously. They’re not waiting for you specifically. If you go dark for a week between rounds, you’re signaling that this isn’t a priority. That’s fine if it’s not, but be honest with yourself about the cost.
Get your decision-making aligned before the search starts. Know who needs to be involved at each stage. Build in calendar time for interviews before you need it. And when you decide someone is the right hire — make the offer. Don’t sit on it for a week running internal approvals. That’s how you lose good people you’ve already invested in finding.
A Note on Staffing and Recruiting Partners
If you’re a company that doesn’t have a strong engineering brand, or you’re hiring in a tight market, or you just need someone faster than an internal search can typically deliver — working with a staffing firm that genuinely specializes in data and tech roles is worth considering.
The key word is genuinely. A lot of agencies claim to specialize in tech but are really generalists who’ll place anyone anywhere. They don’t have the networks or the technical knowledge to evaluate candidates properly. What you want is a partner who can tell the difference between a solid senior data engineer and someone who’s padded their resume, because by the time a resume gets to you, that’s already been sorted.
Ask them about their screening process. Ask for client references. Ask what happens if a placed candidate doesn’t work out. A good partner stands behind their placements and has a process for making it right if something goes sideways.
The Honest Summary
None of this is complicated but it requires actually doing it, not just knowing it. Be specific about the role. Reach out proactively instead of waiting. Write a job description that sells, not just describes. Run a technical interview that tests real skills. Pay what the market requires. And move fast when you find someone good.
Companies that do those things consistently hire well. Companies that don’t, don’t. That’s really the whole thing.
