What to Actually Look for When Hiring Your First Data Person

The Resume That Looks Perfect

A candidate has 5 years as a data analyst, strong SQL and Python, built dashboards, good communication. On paper they're perfect. After 3 months they're struggling — they don't understand your business, they build things you don't need. You were looking for the wrong things.

What the Resume Shows (And Doesn't)

Shows: Technical skills, years of experience, past jobs, projects. Doesn't show: How they think; how they work; whether they understand business; whether they communicate clearly; whether they care about outcome. The second group is more important.

What to Actually Look For

1. Curiosity — They ask questions. "Tell me about your data. How do you make decisions? What's your biggest question?" Red flag: They don't ask any questions. 2. Problem-solving — "If a metric changed unexpectedly, how would you figure out why?" Good: "Check if data source changed, verify calculation, look at business." Bad: "I'd run a query." 3. Communication — They explain technical things simply. Good answer: What, why, how, so what. Bad: all jargon. 4. Business sense — They care about outcomes. Good: "I identified Segment X had 5x churn; team focused retention; churn dropped 30%." Bad: "I built a predictive model." 5. Intellectual humility — "What are you not good at?" Good: "Strong in analysis, haven't done much ETL." Bad: "I'm good at everything." 6. Work ethic — When something went wrong, they identified, escalated, fixed, learned. Bad: "I just told my manager."

Red Flags During Interview

They don't ask questions about your business. They can't explain their work clearly. They blame tools or data for failures. They focus only on technical solutions. They don't ask about your data.

The Test Project

Give them a small dataset and a business question: "Analyze this and tell me what you find." 2-3 hours. You'll learn: methodology, how they think about data, communication, business impact, handling ambiguity. Example: "Here's 6 months of customer data. What can you learn? What questions do you have? What would you recommend?" More useful than an hour of interview questions.

Onboarding (Set Them Up for Success)

Week 1: Business understanding — no analysis yet; learn the business, sit in on calls, read docs. Week 2: Data understanding — walk through systems, data dictionary. Week 3: Small project — low-stakes, you know the answer so you can verify. Week 4: Real projects with support. This takes time but sets them up for success.

Red Flags After Hiring

They're building things nobody asked for. They don't understand the business after a month. Nobody can understand their analysis. They're isolated. Address early; if they persist, wrong hire.

The Downloadable Resource

We've created a Data Hire Interview & Evaluation Guide that includes: Assessment rubric; interview question bank; test project template; reference checking script; red flags checklist; onboarding plan (first 30 days); 90-day success metrics.

Download it here: aiforbusiness.net/resources/data-hire-interview-guide

What's Next

You know what to look for when hiring. But first you need to know whether you're ready to hire at all. The next article, "How to Know When You're Ready to Hire a Data Person (And What Role You Actually Need)," covers the decision.