I’ve made some terrible hires. Brilliant on paper, disaster in practice. And I’ve passed on candidates who seemed risky but would have been incredible.
After building data teams from 20 to 1000+ people at Westpac, and now leading data at Cochlear, I’ve developed strong pattern recognition for what works and what doesn’t.
Here are the red flags I now watch for when hiring data leaders.
Red Flag #1: “I’m a Perfectionist”
What they think they’re saying: “I care deeply about quality.”
What they’re actually saying: “I will bottleneck every decision waiting for perfect information.”
Data leadership requires making decisions with incomplete information, constantly. The perfectionist data leader:
- Never ships because it’s “not quite ready”
- Creates analysis paralysis in their teams
- Misses strategic opportunities waiting for more data
What to look for instead: Leaders who talk about “good enough for the decision at hand” and “iterating based on feedback.”
The question I ask: “Tell me about a time you shipped something that wasn’t perfect. What happened?”
Great candidates have examples. Red flag candidates struggle with this question.
Red Flag #2: Only Technical or Only Business
The too-technical leader:
- Spends all their time in architecture diagrams
- Can’t articulate business value
- Hires only engineers who think like them
The too-business leader:
- Promises things their team can’t deliver
- Doesn’t understand technical constraints
- Loses respect of engineering team
What works: Hybrid leaders who’ve done both. They’ve written code and presented to executives. They understand technical debt and opportunity cost.
The question I ask: “Walk me through a project where you had to make technical tradeoffs based on business constraints.”
Listen for whether they understand both sides deeply.
Red Flag #3: “My Team Didn’t Execute My Vision”
This comes up when asking about past challenges or failures.
Translation: “I’m not accountable for results. I blame my team.”
Great leaders say things like:
- “I didn’t set clear enough expectations”
- “I hired for the wrong skills”
- “I should have course-corrected earlier”
They take ownership. They learn. They don’t blame.
Follow-up question: “What would you do differently now?”
Red flag: They still blame the team.
Red Flag #4: Buzzword Bingo Without Depth
Every data leader knows the buzzwords: AI, ML, Data Mesh, Modern Data Stack, GenAI, etc.
The question is: Have they actually implemented these things at scale?
Warning signs:
- Vague answers: “We used AI to optimise processes”
- No specific metrics: Can’t quantify impact
- No battle scars: Everything went perfectly
- Recently read the book: Just repeating thought leader talking points
What good looks like:
- Specific: “We built a recommendation engine that increased conversion by 12%”
- Honest about challenges: “The first model failed because we didn’t account for seasonality”
- Lessons learned: “I’d approach cold start problems differently now”
The question I ask: “Pick one technology you’re excited about. Tell me about a time you implemented it. What went wrong?”
Great candidates get into technical and organisational details. Red flags stay surface-level.
Red Flag #5: Can’t Explain Technical Concepts Simply
If a data leader can’t explain their work to a non-technical audience, they can’t operate at executive level.
I’m not talking about dumbing things down—I’m talking about clear communication that respects the audience’s intelligence while meeting them where they are.
Test this: “Explain to me like I’m the CFO why we need to invest in data infrastructure.”
Red flag responses:
- Too technical: “We need a Kafka cluster for real-time streaming”
- Too vague: “Data is the new oil, we need to invest”
Strong responses:
- Business outcome focused: “Right now, we can’t answer questions about customer behaviour in real-time, which means we’re losing $X in potential revenue. This investment lets us react within hours instead of weeks.”
Red Flag #6: No Strong Opinions on Organizational Structure
Data leadership requires navigating complex organisational dynamics. How should data teams be structured? Centralized? Federated? Embedded?
Red flag: “I’m flexible, whatever the company prefers.”
This sounds collaborative but actually signals lack of conviction and experience.
What I want to hear:
- “It depends on company maturity, here’s how I’d assess…”
- “I’ve seen both work, here are the tradeoffs…”
- “At my last company we centralized for these specific reasons…”
Strong opinions, loosely held. Backed by experience.
Red Flag #7: Never Talks About People
Some data leaders only talk about systems, pipelines, models, architectures.
They never mention:
- How they developed their team
- How they handled conflict
- How they built culture
- How they retained talent
The reality: At senior levels, the job is 70% people, 30% technology.
What to ask: “Tell me about someone on your team who struggled, and how you helped them succeed.”
Red flag: They can’t think of an example or they talk about performance-managing someone out.
Great answer: Specific story about coaching, development, and growth.
Red Flag #8: Territorial About Data
“Data should report through me. The business can request insights from my team.”
This mindset creates bottlenecks and makes data a service function rather than strategic.
What works: Leaders who are comfortable with:
- Embedded analysts in business units
- Self-serve analytics capabilities
- Federated data ownership
They see their role as enabling the organisation, not controlling access.
Red Flag #9: No Curiosity About the Business
I interview candidates and ask: “What do you know about our business?”
Red flags:
- Clearly didn’t research
- Generic answers: “You’re in healthcare”
- No questions about business model, challenges, or strategy
What impresses me:
- Thoughtful questions about our data challenges
- Opinions on our industry’s data maturity
- Ideas for how data could drive value
Data leaders who aren’t curious about the business will never be strategic partners.
Red Flag #10: Everything They Touch Succeeds
Perfect track record sounds great. It’s actually concerning.
Either:
- They’re not being honest
- They haven’t taken enough risks
- They’re defining success generously
Great data leaders have failures. Projects that didn’t work. Bets that didn’t pay off. Initiatives that got killed.
What matters: What they learned and how they applied those lessons.
The Candidates I Hire
After all this pattern recognition, who do I actually hire?
People who:
- Take ownership of outcomes
- Balance technical depth with business acumen
- Communicate clearly across audiences
- Show genuine curiosity
- Have strong opinions backed by experience
- Admit mistakes and learn from them
- Focus on enabling others, not controlling
- Ship things that matter
These people are rare. But they’re worth waiting for.
If You’re Job Hunting
If you’re a data leader reading this and thinking about your next role:
- Develop business acumen: Understand P&Ls, unit economics, and growth levers
- Practice storytelling: Explain technical work in business terms
- Take ownership: Even when things fail
- Show your work: Have specific examples and metrics ready
- Be curious: Research the company deeply before interviews
The market is tough right now, but great data leaders are still in high demand.
Hiring data leaders or looking for your next role? I’m always happy to share more specific advice over coffee (virtual or real).