You Should Outsource Your Data Engineering
Data engineering and platform development often benefit from outsourcing or hybrid staffing. Learn when to hire consultants vs. full-time engineers for your organization.
By John Wassilak
InfocusData is staffed with Data Engineers that are as senior as possible. We know this because we were all doing the job before the title existed. Before we were data engineers, we were on buzzwordy ‘big data’ teams, and before that we had real jobs.
Our task was to figure out this whole ‘data platform’ thing so that the organization can rake in that sweet data-driven dough. As the space matured with platforms, tools, and practices, we had the opportunity to build dozens of data platforms. Some were successful. Most were not, primarily for reasons we outlined in our Data Strategy series.
Fast-Forward to today, and we’re building a data engineering practice because we think it has similar characteristics to other forms of development:
- Building internally isn’t necessary, and is expensive: Having the ability to own and maintain the outcome makes sense, and should be staffed for, but building internally rarely goes smoothly. Either you repurpose existing resources, paying for years of on-the-job training and oopsies, or you pay up for seasoned engineers that spend most of their time fighting or waiting for you to fix your organizational problems. Then, when initial development is done do you scale down?
- Success depends on expertise: Tools and languages are easier to learn now than ever, but there are a lot of long-term technical decisions to be made and navigating those waters requires experience. Architectural decisions, where to compromise, where to not, how to future-proof, all require years of toe-stubbing to learn.
- Success depends on a variety of skills: Most of which you don’t need long term, or even for the full term of a typical implementation. The economics of the consulting model make it a better fit. Use the platform engineers at the beginning to get setup your infrastructure, train your team, but then roll off. Throw a bunch of data analysts at your initial exploratory data analysis, but then scale it down once things are wrangled.
- Consulting in the data space is dominated by hyperscaler partners: Either you get pushed to lock-in with a hyperscaler ($$$), or you get to pay for some junior consultant’s on-the-job training. In our experience we always got both, and were left with large cloud bills and a half-baked proof-of-concept.
- One-stop solutions only go so far: Tools and platforms have gone a long way, but contrary to what your friendly sales-rep says, most organizations quickly exceed the bounds of what silver-bullet solutions can provide.
When It Makes Sense to Outsource
We’ll spare you the general tradeoffs of hiring FTE’s vs contractors, but that should certainly go into your calculus.
Flexible Staffing
Data engineering work, and data platform work in general, is rarely constant.
You might need intensive effort during a migration project, moderate support for ongoing pipeline maintenance, and surge capacity when building new analytics capabilities. With an FTE, you’re paying full salary regardless of workload. During slower periods, you’re overpaying. During crunch times, you’re understaffed.
If you see yourself needing to scale up, or down, or need specialized skills for a short period of time, you should consider folding in some consultants.
The Need for Speed
A consulting firm working on similar problems weekly can implement solutions significantly faster than an FTE learning as they go. They arrive with established frameworks and templates, knowledge of common pitfalls and how to avoid them.
If you need to hit the ground running, or turn something around fast, an experienced consultant is your best bet.
Everything you’ve tried has failed
An external consultant brings something invaluable that an FTE cannot provide: an outside perspective informed by patterns across dozens of organizations. They can identify when you’re solving a problem the hard way, recommend modern alternatives to legacy approaches, and challenge assumptions about “how we’ve always done it” without the political baggage of an internal employee.
When It Makes Sense to Hire an FTE
Outsourcing isn’t always the answer. There are clear inflection points where having an in-house data engineer, or engineering team, make sense.
You’ve Crossed the Volume Threshold
If your data team is consistently requiring 30 to 40 hours per week of data engineering support with no signs of decreasing, the economics start favoring an FTE. At this utilization level, consulting costs typically exceed the fully-loaded cost of an employee, and you have enough steady-state work to keep someone productively engaged.
You Have Complex Domain Knowledge Requirements
Some business contexts are so specialized that the ramp-up time for any external consultant becomes prohibitive. If your data pipelines are tightly coupled with proprietary business logic, require deep understanding of industry-specific regulations, or involve complex internal systems that take months to understand, an FTE who can build that institutional knowledge becomes valuable.
You’re Building a Data Engineering Function
If you’re at the stage where you need to build a data engineering team of three or more people, it makes sense to hire a lead data engineer as an FTE. This person can provide continuity, manage the strategic vision, coordinate with consultants on specific projects, and eventually build out the team.
The Hybrid Approach: Best of Both Worlds
Increasingly, we see organizations adopting a hybrid model that captures the benefits of both approaches. They might maintain one senior FTE data engineer as the technical lead and system owner, supplement with consulting resources for project work and specialized needs, bring in consultants for quarterly architecture reviews and optimization, and scale consulting hours based on project cycles.
This model provides stability and institutional knowledge through the FTE while maintaining flexibility and accessing specialized expertise through consulting partnerships.
The Bottom Line
For many organizations, particularly those in growth mode with evolving data needs, outsourcing data engineering delivers better outcomes at lower risk. You gain access to broader expertise, maintain flexibility to scale, and accelerate time to value without the overhead and commitment of full-time hires.
The key is recognizing that this isn’t a permanent either-or decision. Many of our most successful projects started with consulting support, scaled their data capabilities, and eventually transitioned to a hybrid model as their needs matured.
The question isn’t whether outsourcing can work, it’s whether your organization has reached the scale and complexity where the economics and operational realities favor building an internal team. For most growing companies today, that threshold is higher than they think.
If you’re still scratching your head about the right approach for your organization, give us a call and we’ll help you figure it out, this is what we do.