Hiring Databricks Specialists: 12 Expert Insights You Need to Get it Right
If you’re thinking of leveraging Databricks and or prepping to start implementation, you’re likely looking at hiring talent (and maybe sweating a bit trying to find the right resource). Bad hires cost the business valuable time and money, so there’s a lot at stake.
We hire Databricks engineers regularly, so we know all the challenges, but more importantly what works and what doesn’t within organizations.
In this blog, we’ll share our experience and insights to help you hire the right Databricks specialists AND be the hero of your development org in the process.
1. Start by Assessing Your Current Team
Do you have a high-functioning in-house development team or just a single engineer on staff? Does anyone have any existing experience working with data or cloud or dev ops?
This is a good starting point to assess the skillsets of your current team and identify gaps. Beyond Databricks specialists, you’re also going to need help from cloud infrastructure experts, data governance specialists, and dev ops engineers, just to name a few.
If these roles don’t exist within your current org, you’ll want to include these in your planning. You may not need all roles full-time, but they will come into play as you implement Databricks.
Finding a single specialist that can handle all…as they say…is like trying to find a needle in a stack of needles at a needle factory.
2. Target the Right Resources
If you are looking to hire Databricks specialists, especially if this is your first Databricks team member, we highly recommend leaning towards mid-strong-to-senior level candidates.
Their depth of experience is needed not only to manage complex data workflows and optimize Databricks but also to help guide you on maximizing the platform and approach. More junior devs will require constant direction and guidance. The price is attractive, but you will run into gaps in both knowledge and experience they cannot overcome alone.
Beyond just seniority, look for someone with a broad range of data warehouse and systems integration experience— Databricks shouldn’t be their first rodeo with data. This is key to ensuring they can handle everything from ETL pipelines to advanced data analytics. The right resource should also be comfortable integrating Databricks with various systems so they can help guide your existing team through the challenges and get all your data sources flowing properly.
3. Consider Blending Talent
Make no mistake, you can find great mid-level talent, but less-than-senior devs cannot know everything and risk wasting time trying different approaches, over-engineering, or even getting fully blocked.
For this reason, we find that pairing a solid mid-level engineer with a senior developer or architect is the best combination. You get the horsepower and drive of young, agile talent with the oversight of more seasoned developers and with another BIG advantage…controlled costs.
4. Set Budget Expectations Internally
Databricks engineers can be expensive, and that’s something you’ll need to budget carefully for.
With Databricks being a relatively niche skill, the demand far outstrips the supply. This means you'll not only face high salary expectations but also potential challenges in justifying the cost if your project is still in its early stages. If this is your company’s first big data project, then it is also likely you won’t have the supporting roles necessary to deliver a complete data solution.
Like any data project, it requires many roles and steps to realize value, so set your budgets and your stakeholder expectations squarely from the start.
5. Provide the Right Direction
A major hurdle when hiring a specialized role like a Databricks engineer is defining clear expectations and direction. If your team doesn’t have deep expertise in Databricks, you risk hiring talent without having a solid roadmap for them. This can lead to frustration on both sides—your new hire might not know where to start, and your team may not be able to provide the direction needed.
It's critical to identify the specific use cases, goals, and needs for Databricks in your business—whether it's data pipelines, advanced analytics, or machine learning—and communicate those goals clearly from the start.
6. Align Hiring Profile with Culture
Know how your company operates and align your candidate profile to it. If your organization takes more of a “sink or swim” approach to hires, then a order-taker-style developer will not work well for you, as they need too much direction and oversight which your org will not prepared to provide.
The best approach is to map your Databricks hiring requirements to the culture of your company. You want your new Databricks specialist to bring an approach that will answer your org’s needs, whether they be working independently or joining a tightly formed and well-managed team.
Keeping your organization’s approach in mind and defining questions to see if new hires align with it will be key to finding the best fit. Don’t fall into the trap that skillset is everything. In our experience approach and culture-fit matter just as much (and sometimes even more than talent alone.)
7. Determine Who Will Manage Them
This is a critical question. In a small team, you may not have someone with direct Databricks experience to manage and mentor this hire.
You need to determine who will manage them technically and ensure they’re aligned with business goals. Without that oversight, you risk underutilizing their skills or them going off in a direction that doesn't meet your immediate needs or larger objectives.
If you are reading this and don’t know, be sure you have this conversation internally. Databricks is a newer platform and requires a unique approach to maximize throughput and results. Truth is, if no one within your organization can provide this oversight, you’ll either need to tackle that problem along with hiring a specialist or consider outsourcing part or all, of your Databricks work.
8. Position to Unblock Blockers
Databricks can get complex quickly—whether it’s handling Spark clusters, optimizing performance, or troubleshooting cloud infrastructure issues. The challenge here is, who will unblock your new resource when they hit roadblocks?
No specialist can know everything and their experience will vary, like why a pipeline fails or how to optimize distributed workloads. You’ll either need to provide the engineer with access to Databricks support, whether in-house or with a consultant. Without unblocking support, progress can grind to a halt, and frustration will grow.
9. Don't Forget Training and Growth
If you do bring in a Databricks engineer, you'll need to ensure they’re not working in isolation. Databricks is a constantly evolving platform, and your new hire will need continuous learning and development to grow with it.
That means giving them access to resources, certifications, and training. But you also need to foster knowledge sharing within your existing team. Otherwise, you risk creating a silo where only one person holds all the expertise, which could hurt your team if that person leaves. Hint: don’t forget to add ongoing training to your resource budget.
10. Prepare Infrastructure and Tooling
Another challenge is making sure your current infrastructure is compatible with the work the Databricks engineer will do. Databricks is a cloud-based platform that integrates with services like Azure, AWS, or GCP, so you need to have these systems already in place.
If your infrastructure isn’t ready, your new hire will be bogged down in setting up systems instead of delivering value. If you don’t have the internal teams or skillsets to handle these areas, this may be a good time to work with a technical partner who does.
11. Balance Expertise and Flexibility
Databricks engineers often specialize in a specific area or set of tasks, whether that’s data engineering, ML pipelines, or data warehousing. But if you’re a small team, you may need them to be more versatile.
Be aware that hiring someone who excels at complex Spark jobs may mean they’re less comfortable with data visualization or DevOps work. Make sure to hire someone who aligns with your immediate needs, but also consider their strengths and weaknesses and have a plan on how you will cover these gaps. Hint: this is often why growth companies use a mix of in-house and outsourced talent to be sure all areas are covered without unnecessary overhead.
12. Ensure Cultural Fit
Lastly, hiring an expensive, highly specialized engineer in a small team can create cultural tensions. Your existing team might feel overshadowed by someone perceived as a "specialist”, or even disconnected if they don’t understand what this new hire is doing, or how it may affect their current role.
It is massively important to foster a collaborative environment where everyone shares knowledge and learns from each other. Introducing a Databricks specialist can rock this boat, so be sure you are setting the stage correctly for everyone to work well together and elevate each other. Specialists need to understand they are plugging into an existing team, and the team needs to be open and ready to collaborate with the new specialist from the get-go.
Final Thoughts and Insights
Every CTO and engineering manager wants their new specialist to be a magic wand that can open new possibilities and solve all problems, but without the right support and structure in place, your risk getting the opposite result.
Ensuring success means being prepared to provide the right direction, oversight, management, and supporting resources for your new Databricks hire. Simply dropping a new Databricks resource into the mix without accounting for these critical dependencies will only get you part of the way there, at best.
Start by assessing your current team, identifying gaps, and using this checklist to compile a complete view of what you have vs what you don’t. Next, meet internally with stakeholders to create a plan that can account for all areas and budgets needed to maximize the resources you hire, whether they are all in-house, outsourced, or a mix of the two.
The bottom line, hiring outside your existing team’s development sweet spot is never easy, but if you plan for the dependencies and gaps covered here you’ll reduce the risk of a bad hire, or worse, spending months spinning your wheels (and burning budgets) without achieving the results the business is expecting.
If you want to learn more or talk with Databricks hiring experts, reach out and contact us and we'll be happy to discuss and share.
