Most articles about how to build a data team are written for companies with a dedicated CHRO, a nine-figure technology budget, and a data organization that already exists. This is not that article.
This is for companies with 200 to 2,000 employees that are realizing the spreadsheet-based approach is breaking down, and need to know where to start without hiring a team of 12.
The Most Common Mistake: Hiring in the Wrong Order
Companies that struggle to get value from their data investments almost always made the same error: they hired in the wrong sequence.
The most common version is hiring an analyst before the data is clean enough to analyze. The analyst arrives, pulls reports, and immediately discovers that the numbers disagree with each other depending on which system you look at. They spend their first six months cleaning data instead of analyzing it, which is frustrating for them and produces nothing for the business.
The second most common version is hiring a data scientist before having a data engineer. Machine learning and predictive models require clean, structured, historical data to train on. If the pipelines do not exist and the data is not reliable, a data scientist has nothing to work with. This is an expensive way to learn that sequence matters.
The infrastructure has to come before the analysis. That is not a philosophy, it is a dependency.
The Right Sequence
Step 1: Understand what you actually need
Before any hire or engagement, get specific about the business problem. Not “we need better data.” That is a symptom description, not a problem statement.
The useful version sounds like: “Our leadership team cannot agree on what our on-time delivery rate is because three systems give three different numbers.” Or: “We have Power BI licenses and no useful dashboards because nobody has connected it to a reliable data source.” Or: “Our CFO wants a monthly margin report by product line and it currently takes three days to produce by hand.”
Those are solvable problems with a clear definition of done. Start there. The answers will tell you what kind of help you actually need and in what order.
Step 2: Get the foundation right first
Data engineering before analytics. Architecture before hiring. This is the sequence that works.
The foundation is the set of decisions and systems that determine whether data is trustworthy: how source systems connect to a central warehouse, how data is cleaned and validated in transit, how pipelines are monitored so failures get caught before a report goes wrong. None of this is visible to end users. All of it determines whether the end users can trust what they see.
Skipping the foundation to get to the dashboards faster is the most common reason data projects stall. You can build the dashboards. They just will not match each other, and leadership will stop looking at them.
Step 3: First hire or engagement
For most mid-market companies, the right first move is a data engineer, either as a hire or through a consulting engagement.
A data engineer builds and maintains the systems that move data from where it is to where it needs to be. They connect source systems, build pipelines, set up the warehouse, and create the infrastructure that everything downstream depends on.
The case for starting with a consultant rather than a full-time hire is that the foundational architecture work is often a defined project. You need someone to make the architecture decisions, build the initial pipelines, and document what they built. That is a consulting engagement. The ongoing maintenance afterward may justify a full-time hire, or it may fit on a retainer. A good consultant will tell you which one honestly.
Step 4: Analytics after the pipes work
Once data is moving reliably and the warehouse has a trustworthy foundation, analytics becomes useful. Before that point, it is mostly frustration.
A BI analyst builds and maintains the reports and dashboards that business users actually interact with. They understand the data model well enough to write queries and build visualizations, but they are not building the infrastructure underneath. That is the data engineer’s job.
A data analyst sits closer to the business questions. They pull data, do ad hoc analysis, interpret results, and help leadership understand what the numbers mean. Both roles are valuable. Both depend on a clean data foundation to do their jobs well.
Hire or engage in that order: engineer first, analyst after the pipes are working.
Step 5: Data science if and when warranted
Most mid-market companies do not need a data scientist, and the ones that think they do usually need a data engineer first.
Data science, meaning machine learning, predictive modeling, and statistical analysis at scale, requires clean structured historical data, a defined business problem that benefits from prediction rather than reporting, and enough volume that a model can learn something meaningful. That combination exists at mid-market scale, but it is less common than the hype suggests.
The honest signal that you are ready: you have clean data, a reporting and analytics function that works, and a specific business decision where prediction would change the outcome. Demand forecasting for a manufacturer with five years of clean order history is a good example. Churn prediction for a SaaS company with well-structured usage data is another. If you cannot name the specific decision, you are not ready.
Build vs. Buy: When to Use Consultants
The build argument: an internal hire develops deep institutional knowledge over time, is always available, and costs less per hour than a consultant over a multi-year horizon.
The buy argument: a consulting firm brings pattern recognition from many environments, can staff up and down with the work, and does not require the overhead of a full-time employee for work that may not be constant.
The honest answer is that most mid-market companies benefit from engaging a consultant to build the foundation, then hiring internally to maintain and extend it. The founding architecture decisions are where experience matters most and where getting it wrong is most expensive. Maintenance of a well-built system is a more defined job that an internal hire can own.
Where this breaks down is when the consulting engagement does not produce good documentation and training. A foundation that only the consultants understand creates dependency rather than capability. Ask any firm you evaluate how they handle handoff and what ongoing support looks like after the build is done.
What to Pay
These are US-based ranges for 2024 and 2025, weighted toward Midwest and secondary markets.
Data engineer: $90,000 to $140,000 base salary. Senior engineers with experience in cloud data platforms and modern tooling (dbt, Airflow, Snowflake, Microsoft Fabric) command the upper end. Entry-level engineers with strong SQL foundations and some pipeline experience start lower.
BI analyst or data analyst: $65,000 to $100,000 base salary. Power BI and Tableau fluency is table stakes. Analysts who can also write SQL and understand data models well enough to work without constant engineering support are worth more.
Data engineering consultant: $150 to $250 per hour for US-based onshore work. Project-based engagements typically run $25,000 to $150,000 depending on scope.
Salary ranges shift with location, industry, and the specific tools your stack requires. These are starting points for budgeting, not guarantees.
Frequently Asked Questions
How many people do you need on a data team?
Fewer than most companies expect. One strong data engineer can support a company of 500 employees with a well-designed architecture. The trap is hiring for headcount before the foundation exists. A team of five analysts working on unreliable data produces less value than one engineer who makes the data trustworthy. Start with quality, not quantity.
Should I hire a Chief Data Officer or a data engineer first?
It depends on what is broken. If the problem is that leadership has no data strategy, no one owns data quality, and the business does not know what it needs from its data, a Chief Data Officer or fractional Chief Data Officer first makes sense. If the problem is that three systems disagree on the same number and reports take three days to produce manually, the pipes are broken and an engineer comes first. In most mid-market companies, it is the pipes.
What is the difference between a data engineer and a data analyst?
A data engineer builds the infrastructure: the pipelines, the warehouse, the connections between systems. They make data available, reliable, and structured correctly. A data analyst uses that infrastructure to answer business questions: pulling reports, doing ad hoc analysis, interpreting trends. The engineer builds the road. The analyst drives on it. Both are necessary, and the engineer almost always comes first.
What if I cannot afford a full data team right now?
A single strong data engineer or a consulting engagement to build the foundation is a better investment than spreading budget across multiple hires before the infrastructure exists. One person who makes your data trustworthy creates more value than three people working with data that cannot be trusted. Start with the foundation and add capacity as the system proves its value.
How do I know if my data is ready for analytics?
A simple test: pick any metric your leadership cares about and try to trace where the number comes from. If you can follow it from source system to report without hitting a manual step or a spreadsheet someone maintains by hand, your foundation is in reasonable shape. If the trace breaks, or if two people give you different answers about how the number is calculated, the foundation needs work before analytics will deliver consistent value.