With AI tools becoming more accessible and more widely understood, almost every business in the world is wondering how to use the technology to their advantage. But harnessing the massive potential of AI isn’t just about using the right prompts. To really make the most of what AI offers, businesses need to start by focusing on the foundation that AI success is built on: data.
To generate useful results, AI needs good data. That means proper management, handling, and storing of data is critical for any business that wants to maximize the value of its AI tools. And this need for robust data management is fueling the popularity of solutions like Databricks.
According to research, 94% of data leaders say they needed to upgrade their data systems this year to fully take advantage of AI. But it’s not just the systems themselves that need investment if businesses are to achieve their AI goals.
Data professionals with the knowledge and experience to navigate solutions like Databricks are also essential to the long-term success of any AI strategy. However, with demand for such talent on the rise, Databricks partners and customers are facing a growing skills gap that’s creating fierce competition in the hiring market.
So what’s driving this Databricks skills shortage—and how can businesses access the talent they need to make the most of their implementations?
The Databricks skills gap in numbers
More and more businesses are turning to platforms like Databricks to help squeeze maximum value from their data. Databricks’ global revenue grew over 50% YOY in the last fiscal year, reaching over $1.6 billion—illustrating the skyrocketing demand for its services. Today, it’s estimated that more than 60% of the Fortune 500 use Databricks to manage their data and power their analytics.
As a result of this growth, demand for the talent that can steer businesses on their AI journey is high, with new job roles being created every day. According to the U.S. Bureau of Labor Statistics, job roles for Database Administrators and Architects (which includes data engineering roles) are expected to grow by 9% by 2033—well above the average growth rate for all occupations, making data engineering one of the decade’s fastest-growing jobs.
The problem lies with the supply. There simply aren’t enough candidates with Databricks experience to fill all these roles. Hiring managers are struggling to find candidates skilled not only in Databricks but in other data engineering, AI, and Machine Learning (ML) platforms and the impact of this shortage is on display across every industry. In a report by MIT, 39% of businesses noted the lack of available ML expertise among the most pressing difficulties they encounter when scaling their ML use cases.
Another survey found investment in talent was the area most businesses wanted to improve about their data strategy, with 39% citing talent acquisition and development as a top priority. In the same report, 40% of businesses said training and upskilling staff to use data and AI platforms was the top problem they faced.
While another report found similar levels of frustration around the data skills gap, with 40% of businesses once again naming the need to train or upskill their workforce to use data and AI platforms as their most challenging pain point.
What's behind the Databricks skills gap?
The shortage of professionals proficient in Databricks is hampering organizations’ abilities to exploit their data and access valuable insights. So why is it so hard for businesses to find, hire, and keep the data engineering talent they need?
The rapid growth of data platforms
A need for specialized skills
High demand for Data Engineers
Shortage of training programs
Salary expectations and competition
Data Engineers, particularly those skilled in platforms like Databricks, are highly sought after and can command high salaries—the average annual salary of a Data Engineer in the US today is around $132,000. Startups and midsized businesses may struggle to compete with tech giants that have the budgets to offer more attractive compensation packages, bonuses, and perks.
High turnover
The complexity of the Databricks ecosystem
How to beat the Databricks talent shortage
With traditional hiring methods struggling to keep up with demand for Databricks talent, and many organizations lacking the time or experience to upskill employees internally, getting the right people on your team can be a real challenge. That’s why Revolent has developed a new way of hiring and training the talent you need.
If you’ve implemented Databricks, you’ll need specialist Data Engineers to help you make the most of this powerful tool, unlock the potential of your data, and maximize your return on investment. As a Databricks Consulting Partner, we can help you find certified Databricks talent with zero upfront investment.
Here’s how it works:
- We recruit experienced candidates or ‘Revols’ tailored to your needs
- We equip them with Databricks skills, certifications, and hands-on experience
- We deploy them to your teams, able to make an immediate impact on your projects
- We continue to invest in their professional development, providing post-deployment training in specializations like Advanced Data Engineering, generative AI, or ML
Why the Revolent way works
Our hire-train-deploy model is an innovative and proven way to access in-demand talent that’s both impactful and cost-effective.
Our model allows you to build out your data and AI capabilities with zero capital investment since all training, onboarding, and development costs are covered by us.
Once we’ve sourced professionals that meet your specific criteria and business needs, we help them build practical Databricks skills and certifications. Our training pathways include the Lakehouse Fundamentals badge and the Apache Spark Developer and Data Engineer Associate certifications, as well as training on Git Flow, advanced SQL skills, Airbyte, FiveTran, and Tableau. And with at least 50% of training time spent on practical application, our Revols are ready to hit the ground running and get right to work on your data projects when they join your team.
During their deployment with you, we continue to develop their skills with additional training, tackling specialisms like advanced data engineering, generative AI, or ML.
On completion of their placement, Revols can convert to your team at no extra cost, helping you mitigate the risk of traditional hiring and boosting your retention rates. In fact, 83% of our Revols convert to our clients’ teams at the end of their deployment.
