Stop Hiring ‘Data Unicorns’—Use a Fractional Squad

“Wanted: Data Superstar – Build and maintain data pipelines; perform in-depth data analysis; develop and deploy machine learning models; provide production dashboard support; create executive dashboards; predict future trends”

We come across dozens of these descriptions every month – typically for an entry-level salary, no less. It demanded everything from a single person – at least 10 different specialised skills spanning across multiple disciplines. The job spec reads like a wishlist written by someone who’s never actually worked in data.

Sound familiar? This approach to data hiring isn’t just unrealistic – it is nefarious for either data infrastructure scalability or the quality of your insights, and it is actively harmful to your business growth. Yet companies continue down this path, wondering why their data initiatives fail before they even begin.

The True Cost of Hunting Unicorns

Recruiting data talent is both slow and expensive. Recent industry reports found hiring a single data scientist in Europe takes 20+ weeks and costs upwards of £10,000 in recruitment fees alone. In the US, the situation is even worse: 40 weeks and over $15,000 before anyone writes a single line of SQL. And that’s assuming you find someone willing to accept your impossible job description in the first place.

That’s not just a statistic, it’s months of competitive advantage slipping away while your data sits untouched. Meanwhile, your competitors who’ve adopted smarter hiring strategies are already extracting insights and making data-driven decisions that directly impact their bottom line.

The Single Point of Failure Problem

Placing all your data needs on one person inevitably backfires. Modern data work spans multiple disciplines:

Data engineering
Infrastructure management & dev ops
Analytics and modelling
Business intelligence
Data science
Business consulting.

Each of these areas requires years of specialised experience to master. Chasing a “data unicorn,” a single hire who can handle all aspects of data work, is fundamentally flawed. Even if such a person existed, they’d command a salary that would make your CFO’s eyes water.

It is not just highly unlikely they’ll be equally great at all skills – it creates a dangerous single point of failure. When that person inevitably leaves (or becomes overwhelmed!), your entire data capability collapses. We’ve seen companies lose months of progress when their “data superhero” burns out or gets poached by a competitor offering a 50% salary increase.

The Squad Approach: Faster, Better, More Resilient

At Tasman, we’ve developed a fundamentally different model. Instead of betting everything on finding and retaining that mythical perfect hire, we deploy a pre-formed fractional data squad consisting of five senior data experts working in concert on your prioritised backlog. These aren’t junior resources learning on your dime – they’re battle-tested professionals who’ve solved your exact problems dozens of times before.

The commercial benefit is clear: you pay roughly the cost of 1.5 full-time employees but receive the complete skill set and bandwidth of an entire data department. Upsize Magazine notes fractional talent models can reduce headcount costs by 30–40 per cent since you only pay for the exact capacity you need. Plus, you can scale up during critical projects and scale down during quieter periods – flexibility a permanent hire can never offer.

The result?

Full bench-strength expertise from day one
Rapid delivery on your most critical data projects
No single person burning out or becoming a bottleneck
Flexibility to scale up or down as your requirements change
Built-in knowledge transfer that ensures continuity even as individual team members rotate.

Three Clear Signs You Need a Squad, Not a Lone Hire

Missed commercial targets: Growth initiatives stall because you lack timely, reliable data to inform strategic decisions. You keep pushing back insights projects because of “resource constraints”.
Perpetually slow-moving data projects: Your analytics backlog keeps growing and deadlines consistently slip despite hiring efforts. You keep seeing data as a cost-centre as they are just firefighting, not producing novel, actionable insights. Your board meetings feature more excuses than insights, and “we’re still building our data capability” has become your least favourite phrase. That “quick dashboard” requested six months ago still isn’t ready, and nobody remembers why it was important in the first place.
High team churn or tool paralysis: Data people keep coming and going, or endless debates about tools and approaches happen without tangible progress. You’ve spent more on recruitment fees than you have on actual data infrastructure, and your Slack channels are graveyards of abandoned tool evaluations.

Real Business Outcomes, Not Technical Experiments

The proof is in the commercial impact. For a bedwear e-commerce company, our squad delivered what multiple individual hires couldn’t: clear attribution connecting marketing spend to actual revenue outcomes, transforming campaign ROI from guesswork to precision. Within weeks, they were confidently reallocating budget to channels that actually drove sales, not just clicks.

For a family photo sharing app, struggling with retention, we quickly identified feature-level churn drivers allowing the product team to fix exactly what was hurting user retention instead of making educated guesses. The result? A massive improvement in 90-day retention that translated directly to revenue growth.

In both cases, a coordinated multi-person team delivered answers in days that would have taken a lone hire months – if they managed it at all. More importantly, they left behind sustainable processes and documentation that kept delivering value long after our engagement ended.