How to hire your first data engineer (and when not to)
The playbook founders and eng leaders need before they make a $150k mistake
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Hi fello data pro, Yordan here,
Last week, I told you how I once spent $8,000 in a single month on AWS.
This wasn’t at some giant tech company. It was a Series A-stage startup. I was the first and only data hire. Looking back, the role should’ve gone to an analyst first.
But here’s the part that really stings.
The mistake wasn’t in who we hired. It was when we hired.
Most early-stage teams make the same misstep. They go looking for a “10x” data engineer when what they really need is clarity, strategy, and some good old-fashioned spreadsheet work.
You might not need a data engineer first. But when you do you better get it right.
Signs you're not ready for a data engineer
It’s easy to get swept up in the hype. You hear about ELT, dbt, Snowflake, or “modern data stacks” and think you’re behind. So you sprint to hire a data engineer.
Pause.
There’s a good chance you’re not ready. Here’s how to tell.
You don’t have tracked events
If Mixpanel or Segment isn’t even wired up, what will your data engineer build? Without tracking, there's no source data. No inputs means no pipelines. This is like hiring a chef before you have a kitchen.
You can answer most questions with GA, Stripe, or HubSpot
If 90% of your reporting lives in third-party dashboards, you’re still in the “off-the-shelf” phase. That’s fine. Leverage these tools. Don’t overengineer. You want to extract value, not build complexity.
No one’s asking for pipelines
Founders think ahead, sometimes too far. But if your team is asking for reports and insights, not infrastructure, don’t answer a reporting problem with an engineering solution. Build for the need, not the resume.
You don’t know what you want them to build
If you’re vague on what this engineer should own, you’re not ready. Engineers need direction. They thrive with clear mandates. “Help us make data better” is not a job description but a red flag.
You and I both know startup time is sacred. Every hire should move a metric or unlock a lever. If you’re still figuring out what your data bottlenecks are, adding an engineer won’t help. It’ll just delay the real work: understanding your own business.
When a data engineer actually makes sense
There’s a point when your scrappy tools and stitched-together dashboards just can’t keep up.
This usually doesn’t happen at launch. Or even after a strong Series A. But eventually, the pain builds. And the signals get loud.
Here’s when it’s time to take the data engineer hire seriously.
You're drowning in fragmented data
You’ve got CSVs coming from marketing. Your ops team is pasting numbers into Notion. Finance pulls exports from Stripe, and product lives in a sea of event logs.
Every team has data. But no one trusts the numbers.
If you spend more time collecting data than analysing it, it’s time to centralise. A data engineer can build pipelines, unify schemas, and give your team one source of truth.
And that changes everything. Decision-making gets faster. Debates disappear. Metrics stop moving mysteriously. You stop firefighting and start forecasting.
You need to model data
There’s a big difference between “pulling a report” and “defining a metric.”
When teams ask questions like “What’s our active user rate?” or “How does retention break down by cohort?”, they don’t only need raw access to data, but a shared definition. A model. Something that persists and scales.
Analysts can write SQL. But engineers design systems.
If your business needs reusable logic, it’s time for engineering.
Because without models, your analysts are reinventing the wheel. Every. Single. Time.
You're asking "what's breaking?" more than "what's happening?"
When dashboards fail, alerts don’t fire, or metrics vanish mid-board meeting. Those aren’t analytics problems. They’re infrastructure problems.
If your current stack can’t stay alive without human babysitting, you’re beyond what analysts can handle.
You need monitoring. Contracts. Versioning. Testing.
These are engineering problems, not spreadsheet ones.
Analytics is blocked by infra
Here’s a dead giveaway: your analysts (or PMs, or growth folks) are constantly waiting. Waiting on ingestion. Waiting on access. Waiting on cleaned data.
If your business questions sit in a queue because your infra can’t deliver, then you’re under-engineered.
And at that point, every day without a proper data engineer costs you leverage. Insights die on the vine. Growth slows. Confidence erodes.
A good data engineer doesn’t just ship pipelines. They remove friction.
And friction is fatal when you’re scaling.
Analyst first, engineer second (and here’s why)
If you’re still early, say sub-50 people, or pre-Series B, your first data hire should almost always be an analyst.
This surprises founders. They want scale. Systems. Real-time dashboards. But those aren’t your first priority. Insight is.
And analysts bring that fast.
Analysts help ask better questions
Engineers solve problems. Analysts help you find them.
A good analyst sits close to your operations. They listen. They translate fuzzy founder goals into real questions. They work backwards from outcomes.
“Why is churn up?” becomes “Which segment saw the highest drop in retention?”
Without this step, you risk engineering answers to the wrong questions. And that’s expensive.
They surface problems that justify engineering effort
You don’t want to build infrastructure because it sounds impressive. You want to build it because you’re hitting walls.
Analysts hit those walls first.
They feel the pain of data spread across tools. They struggle with inconsistent logic. They discover how manual your reporting really is.
These are signals. And when the cost of doing things manually gets too high, you’ll know exactly what’s worth automating.
Now you have a reason to hire an engineer and a roadmap for what they’ll build.
Analysts pave the way for scalable systems
Every dashboard, every SQL snippet, every manual report. Those are prototypes.
They’re the messy proof-of-concepts that teach you what matters. They expose edge cases. They reveal which metrics you trust and which ones always get side-eyed in meetings.
Engineers love clarity. Analysts provide it.
If you try to skip this step, your first data engineer ends up building for guesses. That’s how you end up with brittle pipelines and months of rework.
Bonus: you won't burn a $150k engineer on ad-hoc SQL
This one hurts.
Too many startups bring on a senior data engineer, then ask them to run A/B test queries and marketing attribution reports for the next six months.
That’s not engineering, but reactive analytics.
And it’s a massive waste of talent and money.
Let your analyst clear the path. Let them define what needs scale. Then bring in an engineer to make it real, reliable, and fast.
That’s how you hire for impact. Not just headcount.
If you do hire an engineer, here’s how to not screw it up
So let’s say you’re past the threshold. You’ve got fragmented data, blocked analysts, and manual reporting that’s buckling under growth.
You’re ready.
But hiring the right person is only half the battle. The other half is making sure they succeed.
Here’s how to do that.
Give them a mandate, not just tools
Here’s your Redshift cluster, we use Fivetran and Looker. Good luck.
That’s not a plan. It’s a handoff.
Engineers need a purpose, and not just access.
Instead of listing your tools, describe your problems. Say things like “We want reliable weekly metrics across the funnel” or “We need to track and alert on usage drops in real time.”
Set clear outcomes. Let them design the path.
Don’t treat them like a dashboard dev
This is a common trap. Your engineer joins, and within weeks, they’re writing LookML, fixing broken reports, and joining every stakeholder sync.
That’s not engineering. It’s reactive support.
If they spend all their time downstream, nothing upstream improves.
Give them time to work on systems. Let them stabilise ingestion, enforce contracts, automate quality checks. These things don’t shine in sprint reviews, but they prevent every fire that slows you down later.
Let them invest in infrastructure early
You hired them to build. Let them.
Good data engineers know what debt looks like. They see where pipelines will break, where schema drift will creep in, and where tests will save you from future pain.
Don’t force them to “just ship the dashboard” without thinking long-term.
The right infra investments now will save you six months of rework later.
And trust them to make those calls.
Hire them as a partner, not a reactive ticket-taker
The best data engineers code and shape strategy.
They help define what metrics matter. They push for clean definitions. They ask “why” just as much as “how.”
Treat them like strategic hires. Not service workers.
Include them in planning. Let them challenge assumptions. Give them space to architect and not just implement.
Because once you treat your data engineer like a partner, they’ll do what they do best: build leverage into your company.
What I wish founders knew
Most founders don’t need help spotting data problems.
They know when dashboards break. They feel the pain of stale numbers. They see the Slack threads spiral when a metric doesn’t add up.
What they often miss is this: hiring a data engineer isn’t a silver bullet.
It’s a multiplier. Not a fix.
Hiring data engineers ≠ solving data
Data isn’t just pipelines. It’s people. Process. Priorities.
If you’ve got no ownership over metrics, no clarity around questions, and no agreement on what “success” means, then a new engineer won’t save you.
They’ll inherit the chaos.
You don’t solve data by hiring an engineer. You solve it by aligning your company around truth, speed, and trust. Then you bring in an engineer to make that alignment scalable.
Infra problems are usually people problems
Slow dashboards? Broken pipelines? Conflicting numbers?
Look deeper.
Most of these are symptoms of unclear accountability. No single owner for data. No clear standards. No agreed source of truth.
Founders often look for technical fixes—more tooling, faster queries, better syncs.
But the root issue is coordination. Communication. Culture.
A good data engineer can improve those things. But only if they’re set up to lead, not just patch holes.
Start with clarity. Hire from there
Before you post that job ad, write a brief.
Not for the engineer. For yourself.
Define the problems you want to solve. Name the decisions you need to make faster. List the reports you check every week, and the ones you wish you could trust.
Once you’re clear on what data means to your business, the rest becomes easier.
You’ll hire better. Ramp faster. Ship smarter.
And your data engineer? They’ll actually have a shot at succeeding.
Final thoughts
Hiring a data engineer too early doesn’t just waste money. It creates drag.
You end up with half-baked systems, unclear ownership, and a team still guessing at metrics.
But when the timing’s right? It’s a force multiplier.
The right hire can turn data into a competitive edge. Not just cleaner pipelines, but faster decisions, better products, and tighter feedback loops.
Founders often ask, “When should I hire a data engineer?”
Here’s a better question: “What would a great one unlock for me?”
Answer that, and you’ll know exactly when to hire and how to make it count.
Because building a company without data is hard.
But building one with the wrong data setup is brutal.
So be patient. Get clear. Then hire like it matters.
Because it does.
Until next time,
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