How to Hire Your First Data Person

You've decided you need someone focused on data. But who do you actually hire? The titles are confusing, the job market is competitive, and making the wrong choice is expensive.

The First Question: What Do You Need?

Data roles are specialized. Hiring the wrong type means they'll either be bored or overwhelmed.

Data Analyst - Answers business questions using existing data. Creates reports and dashboards. Works in SQL, Excel, BI tools. Good first hire if you have data but aren't using it well.

Data Engineer - Builds the infrastructure that moves and stores data. Creates pipelines, sets up warehouses, ensures data quality. Good first hire if your data is a mess and needs organizing.

Data Scientist - Builds predictive models and does statistical analysis. Machine learning, experimentation, advanced analytics. Usually not the first hire - you need data foundations first.

Analytics Engineer - Hybrid role. Transforms raw data into clean, documented tables that analysts can use. Bridges engineering and analysis.

Signs You Need a Data Analyst

• You have data in various tools but struggle to answer questions
• Reports are manual and error-prone
• Leadership asks "how are we doing?" and nobody knows
• Decisions are made on gut feel when data exists
• You need dashboards and regular reporting

Signs You Need a Data Engineer

• Data is scattered across many systems with no integration
• You're hitting limits with spreadsheets and manual processes
• Data quality is a constant problem
• You need to build pipelines and infrastructure
• Your current tools can't scale with growth

What to Look For

Technical skills matter, but so does business sense. A technically brilliant person who can't understand business context will build the wrong things.

Look for curiosity. Good data people ask "why?" They dig deeper than the initial question.

Communication is crucial. Can they explain technical concepts to non-technical stakeholders? Can they translate business questions into data problems?

Previous experience in similar contexts. Someone from a Fortune 500 may struggle in a startup (and vice versa). Look for people who've worked at your stage.

Tool proficiency is less important than learning ability. Tools change. Fundamentals don't.

The Interview

Ask about past projects. "Tell me about a time you used data to answer a business question." Look for end-to-end involvement, not just technical execution.

Give a practical exercise. Not a 6-hour take-home. A 1-hour exercise that mirrors real work. For analysts: give them a dataset and questions. For engineers: discuss how they'd approach a data problem.

Test business understanding. "What would you want to know to understand our business?" Good candidates are curious about context.

Assess communication. "Explain [technical concept] to me like I'm not technical." Can they simplify without being condescending?

Common Hiring Mistakes

Hiring too senior too early. A VP of Data with no one to manage and no infrastructure to work with will be frustrated. Start with someone who does the work.

Hiring too junior without support. A fresh bootcamp graduate needs mentorship. If no one can provide it, they'll struggle.

Focusing only on technical skills. SQL wizards who can't communicate or understand business context aren't as valuable as you'd think.

Expecting one person to do everything. "We need someone who can build pipelines, do machine learning, create dashboards, and manage our data warehouse." That's three different roles.

Not having data for them to work with. If you have no data infrastructure, hiring an analyst first is a mistake. They'll have nothing to analyze.

Setting Them Up for Success

Give clear priorities. "Here are the three most important questions we need answered." Don't leave them to figure out what matters.

Provide access. Make sure they can actually get to the data and systems they need. IT friction kills productivity.

Protect their time. Don't let them become the person who pulls every ad-hoc report. Prioritize strategic work.

Get executive sponsorship. Data initiatives need leadership support to succeed.

Define success metrics. What does "doing well" look like in this role?

Compensation Reality

Data roles pay well. In 2024: - Data Analysts: $70K-$120K (depending on location and seniority) - Data Engineers: $100K-$180K - Data Scientists: $110K-$200K

These ranges vary by location and company stage. Startups compete on equity and interesting problems, not just salary.

Alternatives to Full-Time Hire

Contractors/Consultants - Good for specific projects. Get things built, transfer knowledge, then hire to maintain.

Fractional data leads - Part-time senior person to set direction, with junior folks executing.

Outsourced analytics - Some firms (like ours) handle data work on a project basis.

These can bridge the gap while you figure out what you actually need.

Hiring is just the start. Learn about building your data stack and assessing your data maturity.

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Sources: - Bernard Marr: Data Analyst vs Engineer vs Scientist - Glassdoor: Data Analyst Salaries - HBR: What Great Data Analysts Do