Post 03 of 19
The honest math behind the build-vs-buy decision
When companies consider their data strategy, the instinct is often to build in-house. You own the talent, the knowledge stays internal, and you maintain full control. It sounds like the prudent, long-term play. But when you look at the actual numbers — not the headline salaries, but the fully loaded cost over a three-to-five year horizon — the picture is very different. Most build-vs-buy spreadsheets compare the cost of a partner against base salaries for an internal team, and stop there. That comparison is wrong. The real cost of running an internal data function includes recruitment, benefits, tooling, training, attrition, vacancy periods, management overhead, and the opportunity cost of work that does not get done while the team is being assembled. Once those are added in, the gap between perceived and actual cost is rarely under 3×.
The most visible cost of an internal data team is salaries. And they are not cheap. Data engineering and analytics talent has been one of the tightest labour markets in technology for the better part of a decade, and the salary inflation has been particularly aggressive in Western Europe and North America. The numbers below are indicative 2024 total compensation for permanent roles in the US market. Salaries in Northern European hubs such as Amsterdam, Oslo, Stockholm and London are typically lower, although employer taxes and benefits often add more on top.
Senior Data Engineer
$171,000+
Data Engineer
$135,000
BI Developer / Analyst
$95,000–$120,000
Data Scientist
$140,000–$160,000
Data Team Lead / Manager
$160,000–$190,000
Sources: Glassdoor (2024), LinkedIn Salary Insights (2024), Coursera Salary Guide (2024)
A modest team of four — one lead, two engineers, one analyst — is already at $550,000–$650,000 in base salary alone. Before a single dashboard has shipped, before the first cloud warehouse contract is signed, and before anyone has agreed on what 'revenue' actually means across business units. And this team composition is already lean: a serious internal data function for a mid-market enterprise typically scales to six or eight people within eighteen months, because the workload grows faster than anyone projects. Once you add a data scientist for forecasting work and a second analyst to cover finance and operations separately, the salary baseline alone clears $850,000 before any multipliers.
~$4,700
average cost per hire across all roles — before onboarding and ramp-up, and higher for senior specialists
Source: SHRM benchmarking data, 2022 ↗~1.4×
total employer cost relative to wages: benefits make up about 30% of US private-industry compensation costs
Source: U.S. Bureau of Labor Statistics, Employer Costs for Employee Compensation, Dec 2024 ↗When you stack these up, a four-person internal data team realistically costs $800,000–$1,100,000 per year in total. And that assumes everything goes to plan — no failed hires, no extended vacancies, no surprise resignations mid-project. Add tooling (cloud warehouse, BI licences, orchestration, observability) at $80,000–$150,000 per year for a mid-sized environment, plus training budgets, conference attendance, and certifications, and the all-in cost is comfortably north of $1M per annum for what is still a relatively modest data function. None of this delivers a single insight on its own. It is the ticket to entry.
"The salary is the smallest part of the risk. A senior hire who does not work out costs months of momentum, a second search, and work that has to be redone."
— Delta BI
Every month a key data role is unfilled is a month your organisation is flying blind on whatever that role was supposed to illuminate. In fast-moving businesses, that is not a neutral cost — it is an active drag on growth. The data engineering market in particular has an average time-to-hire of around four months for senior roles, and turnover in the first two years is consistently above industry average. The mathematics of running an internal team in this market are punishing: even a well-managed function will spend a meaningful slice of every year either hiring, onboarding, or recovering from someone leaving. A partner model absorbs that volatility entirely, because the continuity sits with the provider rather than with any individual.
A BI partnership like Delta BI gives you a full team — engineers, analysts, strategists, and project leads — at a fraction of the cost of hiring them individually. More importantly, you get:
Immediate productivity
No ramp time, no onboarding delays
Cross-industry expertise
Patterns and solutions tested across dozens of industries and 20+ countries
No single point of failure
A team, not an individual who can resign
Built-in tooling knowledge
No additional licence costs for tools you need to learn yourself
Scalability
Dial up or down based on your current needs
The bottom line
Build-vs-buy is rarely as close as it first appears. When you account for the true cost of recruiting, onboarding, benefits, tooling, and the inevitable turnover cycles, an internal data team carries a price tag most companies dramatically underestimate. A BI partner delivers more capability, faster, for less — and without the operational risk of a key engineer leaving mid-build. There is a place for internal hires, especially for senior analytics leadership and domain-specific roles tied to your strategic agenda. But for the platform layer — pipelines, warehouse, governance, foundational reporting — a partner-led model is materially more efficient. Delta BI delivers this work for a fraction of the loaded internal cost, and stays accountable for the outcome.
Start with the decision
Tell us about your challenge. We will reply to arrange an assessment and identify a practical route to delivery.
Request a data assessment