Quick answer
There are six ways companies get data engineering done. None is universally best. The right choice depends on your stage, budget, and how much business context the work requires. Here's each model, what it costs, and where it breaks.
The six models compared
Full-time hire: $150K–$250K+/year fully loaded. Best when you have daily pipeline work and a team that needs a dedicated lead. Slow to hire (2–4 months), expensive to exit, and most startups under 200 people don't generate enough work to keep one busy year-round. Full cost breakdown.
Freelancer: $5K–$30K per project. Best for clearly scoped, one-off tasks. Fast when it works, but quality varies, there's no business context, and documentation is usually an afterthought. If the freelancer disappears, so does the knowledge.
Fractional engineer: $5K–$15K/month. A senior engineer embeds part-time, learns your business, builds infrastructure, and hands it off documented. Best when leadership wants to build the foundation now and hire in-house later, or needs ongoing maintenance without a full headcount. Not available 5 days a week, and quality depends on the individual. Vet like a full-time hire. Cost details.
Agency / consultancy: $20K–$50K+/month. Best for large, well-defined projects (platform migrations, compliance overhauls). Expensive, incentivized to extend engagements, and the senior architect who sold the deal may not be the one doing the work. Overkill for most companies under 200 people.
Managed service: $8K–$25K/month. You hand operations to a third party who monitors pipelines and fixes failures. Best when the stack is stable and you want guaranteed SLAs. You don't own the knowledge. If you leave, you may not understand your own system.
DIY platform (Fivetran, dbt Cloud, Airbyte): $500–$3K/month in tooling. Best for early-stage teams with simple, standard sources. Works until complexity grows: custom transformations, cross-source joins, or schema changes that break silently. No one owns the architecture.
For a deeper dive on the two most common options, see fractional vs full-time data engineer.
Want senior data engineering without the full-time commitment?
A fractional data engineer embeds in your team part-time, builds production-grade infrastructure, and hands it off fully documented.
Learn how fractional works →How to choose
Ask four questions:
- Is this a project or ongoing work? Projects (build the warehouse, connect 5 tools) → fractional or freelancer. Ongoing daily work → full-time or managed service.
- How important is business context? If the engineer needs to understand your revenue model or compliance requirements, freelancers and DIY platforms won't cut it. You need someone embedded.
- What's your budget? Full-time and agencies sit at the top. Fractional in the middle. Freelancers and DIY at the bottom, but true cost rises when you factor in management overhead and rework.
- What happens after it's built? If your team needs to maintain it independently, you need someone who documents and hands off. Managed services and agencies often don't optimize for this.
What most startups actually do
- Start with DIY. Fivetran syncs a few sources, someone sets up Metabase. Works for 6–12 months.
- Hit the wall. Reports stop matching, analysts spend more time cleaning than analyzing. Tools work individually but nothing connects.
- Bring in a fractional engineer. They build the real foundation: warehouse, transformation layer, documented pipelines. Fully handed off, with ongoing maintenance for as long as it makes sense.
- Hire full-time when ready. Once the system is stable and you know the role, you hire someone who inherits a clean, documented system instead of a mess.
The mistake is jumping to step 4 before step 3: hiring full-time to build from scratch is slow, expensive, and risky if you don't yet know what "good" looks like for your setup.
There's a reason this works: data engineering follows established patterns (pipeline architecture, warehouse design, transformation frameworks) that transfer across companies. The technical work doesn't need to be reinvented in-house. What matters is someone senior enough to make the right decisions for your specific business, then hand it off. Data analysts, on the other hand, need to live inside your business permanently: they interpret metrics, answer questions, and build context that compounds over time. That's worth a full-time seat. The infrastructure underneath is a build-and-maintain engagement, not a permanent role.
Start with the roadmap below to see which model fits your situation.