Last updated: July 12, 2026
By Tom Kenaley, President & Senior Partner, KORE1
Build a data team in hiring order: start with one analyst or analytics engineer to make your existing data usable, add a data engineer when pipelines break, and hire a data scientist only after you have clean data. A dedicated data leader comes around four to six people, not on day one. Sequence beats speed, and hiring out of order is the most expensive mistake founders make.
That order sounds obvious written down. Almost nobody follows it. The most common way I see a first data team go sideways is a company hires a data scientist first, hands them a mess of spreadsheets and a half-broken export from their billing system, and waits for magic. Six months later the scientist is bored, the dashboards still do not agree with each other, and the CFO is asking what the salary bought.
Here is my stake in this. We place data talent, and we get paid when you hire through our data engineering and data science staffing practice. So I have a thumb on the scale, and you should read the rest knowing that. I am still going to tell you, near the end, about the situations where you should not call us at all. That part is the one that keeps clients coming back.
Start With the Problem, Not the Headcount
Building a data team means hiring the people who turn raw, scattered company data into decisions leadership can trust, in the order that each new hire actually unblocks the last one. It is a sequence, not a shopping list. Get the sequence wrong and you pay senior salaries for work that stalls.
Back in 2017, Monica Rogati drew a picture that still explains most failed data teams better than anything since. She called it the AI Hierarchy of Needs, a pyramid modeled on Maslow. At the bottom sits boring plumbing. Collecting data. Moving it. Storing it somewhere you can query. Machine learning and AI sit at the very top, and you do not get to eat there until the bottom of the pyramid is fed.
Most companies want to start at the top. Who wouldn’t? The top is where the interesting words live.
The numbers say that instinct is expensive. Gartner expects organizations to abandon 60% of AI projects through 2026 because the underlying data was never made ready for them. Let that land. It is not the model that kills the project. It is the foundation nobody wanted to build first.
The First-Hire Mistake I See Most
A logistics company outside Austin called us two years ago. They had raised a Series A, the board wanted “an AI story,” and they had already posted a job for a senior data scientist at $185,000. Good intentions. Wrong first move.
I asked one question on the intake call. Where does your data live right now? The answer was four SaaS tools, a Postgres database nobody had touched in a year, and a shared drive full of spreadsheets named things like final_v3_ACTUAL.xlsx. There was no warehouse. No pipeline. No single place where a number meant one thing.
A data scientist walking into that spends their first quarter doing janitorial work they hate and were not hired for. We told the client to pause the req. Instead we helped them hire an analytics engineer who set up a real warehouse in Snowflake, wired their tools together with Fivetran and dbt, and had trustworthy revenue and churn numbers flowing in about ten weeks. The scientist came a year later, once there was something to actually model. She was worth every dollar by then. Worth the wait, too. Timing was the whole game.
The lesson is not “never hire a data scientist.” It is that the title on your first req should match the work your data actually needs, and early on, that work is almost never data science. There is a genuine consensus on this among people who have built these teams. Your first hire should make the data you already have usable. Everything else waits.
The Build Order: Who to Hire, and When
Below is the sequence we walk clients through. Read the trigger, not just the title. The trigger is the signal that tells you it is time for the next hire. Hire ahead of the trigger and you burn salary on someone underused. Hire behind it and the team drowns.
First Hire: Someone Who Turns Data Into Answers
Title this one a data analyst or, if you can afford the skill, an analytics engineer. The difference matters. An analyst answers questions with SQL and a tool like Tableau, Looker, or Power BI. An analytics engineer does that and builds the clean, modeled tables underneath so the answers stay consistent as you grow.
What you want on day one is someone who can walk into the mess, pick the three metrics the business actually runs on, and make those three numbers mean one thing across the company. That is worth more than any model in year one. By a mile.
Hire this person when leadership is making calls off gut feel and spreadsheets that disagree with each other. So, basically now.
Second Hire: Someone Who Keeps the Pipes From Bursting
The trigger for your second hire is failure. Specifically, the analyst starts spending more time fixing broken data loads than answering questions. Pipelines that ran fine at 50,000 rows fall over at five million. A source system changes a field and three dashboards silently go wrong for a week before anyone notices.
That is when you bring in a data engineer. Their job is the plumbing the analyst has been duct-taping. Ingestion, the warehouse, orchestration in something like Airflow, and the unglamorous discipline of making sure last night’s load actually finished instead of just looking like it did. Our data engineering staffing desk fills more of these roles than any other data title, and it is almost always the second hire, not the first.
Skip this hire too long and your analyst quits. I have watched it happen. More than once. Good analysts do not stay in a job that has quietly become unpaid infrastructure work.
The Data Scientist Comes Third, Not First
By now you have clean data flowing and a place to query it. Now, and only now, a data scientist earns their salary from week one. There is a real forecasting problem waiting, or a churn model that would genuinely change how you spend next quarter, or a pricing question that needs actual statistics instead of a confident hunch from the founder. Real problems, real data, immediate value.
The Bureau of Labor Statistics puts the 2024 median wage for data scientists at $112,590 and projects the role to grow 34% through 2034, from roughly 245,900 jobs to 328,300. Fourth fastest-growing occupation in the country. That demand is exactly why you do not want to burn a scientist on plumbing. You are competing hard for them, so give them the work they signed up for. If you want the full picture on what these hires cost, we keep a data scientist salary guide updated for 2026.
Around Six People, You Need a Boss for the Data
Three or four individual contributors can self-organize. Six cannot. Priorities collide, two people build the same table, and nobody owns whether the company’s numbers are actually right. That is the trigger for a data leader.
What you call the role depends on scope and stage. A hands-on team lead for a small group. A head of data for a growing org that needs strategy and headcount planning. A VP or chief data officer once data is a board-level concern. This person owns the roadmap, the hiring, and the uncomfortable job of telling the CEO that the answer to their question will take three weeks and not three hours. That job matters.
Specialize Last, Once the Work Demands It
After the core is in place and humming, the team splits along the work. A machine learning engineer to put models into production. A data product manager to own metrics and priorities. A governance or privacy specialist once you are handling regulated data. None of these are early hires. Reach for them when a specific, recurring pain makes the business case on its own, not because some org chart you found online says a “complete” data team is supposed to have one of each.
Once you have made the first three or four hires, the next question is how they report and relate. We wrote a whole separate piece on how to structure your data org once it exists, because structure is a different problem than sequence, and this post is about sequence. Building the AI side of the house instead? The same sequence-over-titles logic drives how to build an AI team from scratch.
The First Five Data Hires, In Order
Here is the whole sequence on one screen. The base ranges are U.S. figures we cross-check against Levels.fyi, Built In, and Glassdoor, then against the offers our clients actually sign across more than 30 U.S. metros. Local markets swing these bands by 20% or more, so treat them as a starting frame, not gospel.
| Order | Role | Hire When | What They Own | US Base Range |
|---|---|---|---|---|
| 1 | Analyst / Analytics Engineer | Leadership decides off conflicting spreadsheets | The three numbers the business runs on | $95K to $150K |
| 2 | Data Engineer | Pipelines break; the analyst is fixing loads, not answering questions | Ingestion, warehouse, orchestration | $120K to $180K |
| 3 | Data Scientist | Clean data exists and a real modeling problem has value on the table | Forecasting, ML models, statistical analysis | $130K to $200K |
| 4 | Data Leader (Head of Data / VP / CDO) | Team hits five or six people and priorities collide | Roadmap, hiring, data trust across the company | $180K to $300K+ |
| 5 | Specialists (ML Engineer, Data PM, Governance) | A specific recurring pain justifies a dedicated owner | Production ML, product metrics, compliance | $140K to $250K |
Notice what the table implies about budget. A credible first data team of three, an analytics engineer, a data engineer, and a scientist, runs you roughly $350,000 to $500,000 a year in base salary alone before benefits, tooling, or a leader. That is real money for a company that just raised its Series A. It is also why the sequence matters so much. Every hire made out of order is a six-figure bet placed before the table was ready.
Build, Borrow, or Outsource It?
None of this has to sit on your own payroll. Sometimes it should not. You have three honest paths, and which one fits depends almost entirely on how central data is to the way your company actually competes for customers.
- Build. Full-time employees, on your payroll, learning your business deeply over years. Best when data is core to how you compete and you can keep them busy and growing. This is the right answer for most companies past Series B.
- Borrow. A fractional head of data one day a week to set direction, or a contract and contract-to-hire engineer to stand up your warehouse without committing to a permanent seat before you know the shape of the work. We see this constantly with companies that need senior judgment now but cannot justify a full-time senior salary yet.
- Outsource the plumbing. Managed pipeline tools and an agency or consultancy can run ingestion so you skip the first engineer for a while. Fine as a bridge. Risky as a destination, because the institutional knowledge walks out the door with the vendor.
Here is the part where I talk myself out of a fee. If you are a 15-person company that needs a dashboard refreshed twice a month, you do not need a data team and you do not need us. Buy a BI tool, teach an ops-minded person some SQL, and revisit in a year. A staffing firm that tells you to hire when you do not need to is not a partner. It is a vendor. We would rather you call us when the need is real.
How Long It Takes and What It Costs to Get Started
Standing up the first hire is faster than most founders expect and slower than they want. A good analytics engineer or data engineer search runs four to eight weeks from open req to signed offer in a normal market, longer for a specialized stack or a senior leader. Our average time-to-hire across IT roles is 17 days once we have a clear req and a decisive hiring manager, and the roles that drag are almost always the ones where the client cannot decide what the first hire should actually own.
Retention is the number that should worry you more than speed. Data people leave fast when the work is not what they were promised, and replacing a specialized hire six months in resets your whole timeline. It is one reason we hold a 92% twelve-month retention rate on our placements. Matching the hire to the actual stage of your data, the thing this entire post is about, is most of what protects that number. If you want to sanity-check a band before you post, our salary benchmark assistant will get you close, and for a first scientist specifically we break down the full loaded number in our guide to hiring your first data scientist.
Questions Founders Ask Us Before They Build
Who should my first data hire actually be?
An analyst or analytics engineer, in almost every case. Their job is to make the data you already have trustworthy and usable, which is the foundation every later hire depends on. A data scientist first is the single most common and most expensive mistake founders make.
How many people do I need for a real data team?
Three is a functioning team. Five is where you need a leader. Most companies get a lot of mileage from an analytics engineer, a data engineer, and one scientist before adding anyone else. Headcount should follow specific pain, not an org chart you saw online.
When is it finally time to hire a data scientist?
When you have clean, flowing data and a modeling problem worth money. If your scientist would spend their first quarter building pipelines and cleaning spreadsheets, you are not ready, and you are about to waste a scarce and expensive hire on work they will resent.
Should I just outsource the whole thing instead?
Outsource the plumbing early, own the judgment always. A managed pipeline vendor or fractional lead is a fine bridge while you are small. The risk is that your business logic and institutional memory live with a vendor who can walk, so plan to bring the core in-house as data gets central.
What does a starter data team cost per year?
Roughly $350,000 to $500,000 in base salary for a first team of three, before benefits, tooling, and a leader. That is why sequence beats speed. Every hire made before your data is ready is a six-figure bet on work that cannot start yet.
Do I need a data engineer or a data scientist first?
Neither, usually, before an analyst. But between those two, the engineer comes first. You need trustworthy pipelines before modeling on top of them means anything, which is why the data engineer role tends to land as hire number two and the scientist as hire number three.
The One Rule That Survives Every Situation
Build the bottom of the pyramid first. Feed the plumbing before you reach for the model. Hire the person who fixes what your data actually needs today, not the person whose title sounds most like the future you are pitching investors.
We have built and staffed data teams across more than 30 U.S. metros for companies at every stage, from a founder making their first data hire to a public company rebuilding a neglected org. When you are close to that first hire and want a gut check on who it should be, talk to a recruiter on our team, and you will get a straight answer whether or not it ends with you hiring us. Even if the answer is wait. Especially then.