Being the first data hire at a startup is usually chaotic, and everyone wants (or in their words, "needs") something from you. But that's the job. After a few tours of duty, here are some of the biggest lessons I've learned.
Lesson 1: Build scalable data models up-front
If there's anything I've learned about working at VC-backed startups as a single-person team, it's that building a robust and scalable data model up-front is everything. The trick is building it at the exact same time as you move the business forward.
Why is the model so important? Because it's a living organism that encapsulates everything your business knows and does in one place. Your job as the "data team" is to maintain that living organism. Feed it well, walk it regularly, and keep it clean. The smarter, cleaner, and happier that model is, the better the business decisions and product features it generates and the easier your job will be.
Now, describing how to build a good data model could fill a book. But at its core, you build it problem by problem, solution by solution, as the business grows.
For example: that week-one request from Customer Success about low-engagement customers isn't just a report. It’s an opportunity to define the fundamental customer and product schemas and logic within your model. You don’t need the entire model on day one, but you should start laying the main pipes and tables everything else will build on.
Lesson 2: It's not really about data
The job isn't as much about data as it is about building systems that create leverage. The leverage you create shows up as faster and smarter decisions. In other words, you're a force multiplier and there’s almost nobody in the business whose decisions you can’t improve in one way or another.
The systems you create generally fall into one of two categories:
- Automation -> faster decisions
- Enrichment -> smarter decisions
And, given today’s junk drawer of tools that startups rely on, there are plenty of opportunities to create systems... throw a rock: there's a CRM full of junk, Mixpanel or PostHog tracking data only engineering can access, Customer Success software floating around, support tickets piling up somewhere else, and that’s before counting the random software someone expensed on a corporate card two years ago. Where should you start? Start where customers feel the most pain: churn risk, adoption health, support backlogs, etc.
But that's it. That's the whole job: create leverage, not numbers. Numbers alone are noise.
Lesson 3: Don't waste time chasing perfection
I’ve spent a lot of time in my career trying to be 100% right. Truth is, though, unless you’re building rocket ships or surgical software, you usually don’t need to be more than 80% right. Often, even less is fine. How accurate you need to be depends on the problem. But, a good CEO I know said it best: "there aren’t many moats left in this world... so, speed wins and directionality is underrated."
If you’re unsure, look at the average early-stage startup: they don’t have infrastructure or data. They run on coffee, Excel, and executive intuition. Almost anything you build is already a big step forward. Take that step, then iterate, if needed.
To emphasize the point: I promise you, going to a stakeholder quickly with a direction and an invitation to keep digging once this direction has been exhausted will be an order of magnitude better than taking the time to find the perfect path. Your stakeholders need to make decisions today, not later today. Give them something now.
Lesson 4: Own Fires
Startups are effectively a forest full of fires. You only see the fires in your neck of the woods, but they're all over the place.
Now for those fires you can see: own them. Why? For lots of reasons. First and foremost, because things generally need to get fixed and you just happen to be in the fixing things business. Additionally, if you don't own the fire, you don't know if it needs to be put out. Sometimes fires can wait. But, many times they can't.
Not long after joining my first startup, I ignored an important upstream pipeline that fed our product after being enriched by an ML model. The ML model was new and spitting out garbage, so I focused there, assuming the pipeline was in decent shape, having been built before I joined the team and thinking I’d get help with it. Spoiler: the pipeline was the real issue (as we all know, "garbage in, garbage out")... and no help ever came. I learned the hard way that, at an early stage startup, everything is your job.
The lesson here is simple: own things, even the ones you're not supposed to. At worst, you learn something new. At best, you now own something else, and you learn something new. Fun!
Lesson 5: Treat Your Data Model Like a Product
Did I mention the data model? Let me say it again. Everything comes back to your data model. Want to move faster? Fix the tech debt. Want to build a custom app for the customer team? Finesse the data model. Someone asks you for a number? Add it to the model, because more likely than not, it'll get asked for again.
If you're still not convinced, well, LLMs are solid, and that data model you've been carelessly hacking together isn't just for you or human consumption anymore. If you want to stay ahead, or at least avoid falling behind, optimize what you feed your LLMs. Right now, your LLM is only as good as the context you give it. Feed it garbage and... you know the rest. That means documented schemas, tested pipelines, and a semantic layer with approved metrics and definitions, not just ad hoc tables.
Closing
Being the first data hire isn’t about dashboards, writing SQL, or cranking out product metrics. It’s about building systems that make better answers possible. Whether your title is analytics engineer, data engineer, or data scientist, your actual role is to enable better decisions and do whatever it takes to execute that role.
Your job is to help everyone else do theirs better. Do that well, and you won’t just avoid being consumed by the startup chaos, you’ll shape the company’s direction.
To recap:
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Build scalable data models early. They’re the backbone of every decision.
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Focus on creating leverage through systems, not just reporting.
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Speed and direction matter more than accuracy in most startup contexts.
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Own fires, even ones outside your lane. You’ll learn faster and prevent bigger problems.
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Treat your data model like a product: documented, tested, and ready to feed both humans and machines.
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Your real role: enable better, faster decisions and make everyone else’s job easier.