If you're considering IT infrastructure outsourcing, choosing the right partner is essential. According to data from Cisco, outsourcing IT can reduce recurring in-house costs by up to 40% and increase efficiency by 50-60%. However, if your outsourced IT infrastructure team isn't a good match for your company, you may waste valuable resources and create bottlenecks.
When you're evaluating a partner for IT infrastructure outsourcing, it's obvious to assess their technical capabilities. Yet many companies overlook the non-technical aspects of a partnership, which can be just as impactful for the success or failure of their IT infrastructure initiatives. An ideal outsourcing partner will seamlessly integrate within your workflows, communicate clearly, and scale predictably. They'll treat your business like their own.
Unfortunately, sometimes it's not easy to spot the difference between a vendor who simply supplies staff and a strategic partner whose team will go the extra mile. That's why we've prepared this helpful guide to evaluate IT infrastructuring outsourcing partners. Read on to learn about the essential questions to ask and what to look for in a partnership.The data and AI revolution has changed how businesses operate. According to a recent study by Informatica, 67% of industry data leaders report roadblocks when transitioning AI pilots to production. Staffing shortages often cause these roadblocks. Hiring data engineers can be a long, expensive process that slows time-to-market and creates bottlenecks.
We often hear from companies facing the same dilemma. They're unsure whether to pursue traditional in-house hiring or seek talent externally. The decision to hire in-house versus outsourcing data engineers is more complex than a simple cost comparison. It's a strategic choice that can impact all facets of your business.
If you need help understanding the trade-offs among cost, speed, and risk across different data engineer hiring models, this guide is for you. We'll break down the decision criteria you need to choose the best path forward. Whether you need a data scientist, a generative AI expert, or another specialist in data engineering, make sure you hire the best for your organization's needs.
The Tradeoffs of Hiring Data Engineers In-House
Hiring data engineers in-house offers control, but it requires hefty investment and patience.
Costs of In-House Hiring
The hiring pool for data engineers is highly competitive. Even at the lowest end, starting salaries for data engineers average $100,000.
But this base pay is only the beginning of your in-house expenses. With benefits, training, and time lost during the lengthy recruitment process, onboarding a full-time data engineering employee requires a lot. Common expenses involved in hiring data engineers include:
- Recruiting Fees: Often $20,000 to $40,000.
- Benefits and Taxes: An additional 20-30% of the base salary.
- Onboarding and Equipment: Expenses for training and setup.
Altogether, the total first-year cost for hiring a specialized AI or data engineer in-house can quickly climb to approximately $150,000 to $200,000.
Impacts on Timelines
The time required to onboard a specialist is another major hurdle. From drafting compelling and comprehensive job descriptions to reviewing applicants and conducting interviews, each day you spend looking for a new hire is a day you could've spent on project development or other aspects of your business.
Also, AI and data jobs are currently among the most difficult skill sets to find in qualified applicants. With the shortage of specialized talent, finding a full-time team member can take weeks or even months.
The Strategic Advantages of Outsourcing Data Talent
Due to the challenges described above, many organizations turn to staff augmentation for hiring data engineers. Staff augmentation offers a faster, more flexible, and more cost-efficient model for acquiring specialized talent.
With the right staff augmentation partners, you'll find external professionals who integrate directly into your in-house team with the right expertise. Outsourcing also provides easy scalability. Do you need to hire data engineers for a one-time campaign? Outsourcing can offer talent on an as-needed, temporary basis to fulfill special projects. It's a great option to temporarily replace team members on medical or family leave, as well.
Other strategic advantages of outsourcing data engineers and other AI talent include:
Predictable Costs and Value
Outsourcing through a subscription-based staff augmentation model, like the JADA Squad’s, eliminates many of the volatile aspects of traditional hiring. Rather than paying hefty placement fees or covering benefits, you operate on a fixed monthly subscription.
This predictable model makes budget planning economical and allows for easy scaling up or down as your technology priorities change. This model can provide access to world-class talent at a fraction of the cost and time of traditional recruiting.
Rapid Deployment and Specialized Expertise
Speed is one of the key advantages of hiring data engineers externally. With an outsourcing partner, you can rapidly increase your team and scale projects more quickly. It allows for rapid talent embedding, often within 5-10 business days.
Providers like the JADA Squad maintain a curated, well-qualified talent pool with professionals who possess the perfect skillset for complex projects involving data science, generative and agentic AI, and MLOps. This immediate access enables you to rapidly tackle any project.
Risk Mitigation and Flexibility
Finally, the risk associated with a bad hire is high, especially in complex, niche fields like agentic AI or machine learning. Outsourcing mitigates this risk through rigorous technical and cultural vetting, combined with an ongoing partnership that includes continuous support.
If the fit isn't perfect, an outsourcing model allows for a flexible team structure, letting you easily adjust the composition of the team as your needs evolve.
When to Outsource Versus Hiring Data Engineer Experts
So when should you outsource? When should you pursue a traditional in-house hiring process? The right choice will depend on your current data maturity, the scope of your projects, and your financial needs.
Choose Outsourcing When:
- You need specialized skills fast: If you are urgently scaling a new initiative, such as a generative AI project, and cannot afford the long recruitment window.
- Your needs are project-based or variable: When you need to scale up for a quarter for a new product launch, and then scale back down, the flexible team structure of staff augmentation is ideal.
- Your data is already organized: If your infrastructure is clean and you need a data scientist to analyze it, outsourcing a data scientist provides immediate access to high-value insights.
- You need cost predictability: When you must manage tight budgets and require fixed, predictable monthly expenses over the variable, high cost of an in-house team.
Choose In-House Hiring When:
- Your data is a long-term core competitive advantage: For foundational roles where deep, multi-year institutional knowledge is non-negotiable.
- Your data is disorganized and messy: If your data is scattered and inconsistent, a full-time data engineer is needed to establish the foundational data architecture, data lakes, and ETL processes first. The process of hiring data engineer talent is foundational for future data science work.
- You require direct, granular management and control: If the data work is highly proprietary or requires day-to-day, on-site supervision.
Scale Smarter with The JADA Squad
In today's fast-paced world, your data and AI needs can't wait. If you're not scaling rapidly, you'll lose ground to the competition. So don't let the complexity of hiring data engineers or data scientists' expertise create barriers to your company's growth.
The JADA Squad offers a smart, fast alternative to traditional hiring. Our specialists can integrate directly into your team and accelerate your data and AI initiatives. Contact JADA today to learn more.
Frequently Asked Questions
Is there a high demand for data engineers?
Yes. There is high demand for data engineers because companies need these expert professionals to build and maintain data and AI infrastructures. As organizations become more and more dependent on data, the need for skilled data engineers will only grow.
Is AI replacing data engineers?
No. Although AI can automate tasks like data cleaning and pipeline monitoring, this technology still requires data engineers to design the systems that manage data and ensure data quality. Instead of replacing data engineers, AI is used as a tool that makes their role more efficient.
What is the all-in cost of hiring a specialized data engineer?
While costs can vary depending on the local market, the average first-year cost for hiring a full-time, in-house data engineer is $150,000 to $200,000 annually.
How quickly can I get a data expert through outsourcing versus hiring?
Outsourcing via staff augmentation allows for rapid deployment, often within 5-10 business days. Traditional in-house hiring, especially for specialized AI roles, can easily take months due to the lengthy recruitment and vetting process.
What is staff augmentation, and how is it different from traditional outsourcing?
Staff augmentation integrates specialized external talent directly into your existing team to work under your direct management. Traditional outsourcing, on the other hand, delegates an entire project to an external company to manage and execute independently.
