Even in the age of GenAI, data professionals are still in high demand across a wide array of companies, functions, and industries.
One way to capitalize on this demand is by exploring the world of freelancing in Data.
Here’s a few reasons why you should consider freelancing as a data analyst, data engineer or data scientist:
There are no shortage of well-paying jobs
Go ahead and search Upwork, Fiverr, or any other freelancing platform, and you’ll find lots of jobs in data engineering, data analytics, and data science.
As you get more specialized, and/or develop more advanced skills, this becomes even more true.
Clients are always looking for freelancers who are highly skilled, efficient, communicate well, and can solve their specific problem.
And in most cases – they’re willing to pay a premium for it.
Here’s a quick search on Upwork for some examples:
Data engineering:
Data analysis:
Data science:
So what’s the upshot? There’s good money to be made if you’re highly skilled.
It Offers Flexibility
Freelancing gigs are usually remote, shorter term and contract-based. This obviously comes with the downside of not offering the stability and comfort of a full-time job, but it also offers a lot of benefits:
- You don’t have to commute
- You’re free to decide what you do and when
- You can (and should) work with multiple clients at the same time
- You can (and should) diversify into other areas as well (e.g. selling digital products, coaching, online courses, etc.)
Also, if you got laid off from your full-time employer, or are in between jobs – freelancing (even just on a short-term basis) can help pay the bills while you look for a new job.
No cap on your success/earnings potential
Since as a data freelancer, you can work for multiple clients, the only limit on your earnings potential is your time and skills.
Of course, if you’re working hourly contracts, you’ll always be limited by the hours you work.
But if you work hard and have enough clients
Skills will drive the hourly (or fixed price) rate you can charge. As your skills become sharper, more specialized or more advanced, you can gradually increase your rates.
If you’re really good and in an in-demand niche, you can earn $100/hr, $120/hr, even $150+/hr or more.
For context, here’s what those look like equated to an annual salary, working 48 weeks at 40 hours per week:
- $100/hr – $192,000
- $120/hr – $230,400
- $150/hr – $288,000
Keep in mind, this doesn’t include taxes, health insurance, etc. (and also assumes you always have projects to work on, which may not be true), which you’re all on your own for as a freelancer, but still, this is pretty amazing earning potential.
Bump your work hours up to 50/hrs per week (not out of the ordinary for a full-time job), and you’re looking at:
- $100/hr – $240,000
- $120/hr – $288,000
- $150/hr – $360,000
Keep in mind, it’s going to be a lot of hard to work and earn this much consistently as a freelancer, and you’ll have to build up to this once you’re more experienced.
But if you ask me, these would be pretty nice earnings numbers!
You learn new and different skills much faster
This boils down to the fact that as a data freelancer, you’re not stuck with one tech stack, set of tools, or business domain.
As you work with more and more clients, you can expand your skillset dramatically.
Even if you stay in a relatively small niche or only use one set of tools, you will still get exposed to a lot of different datasets, use cases, and business domains.
For example, a week as a freelancer might look like this:
- 5 hours marketing your services, e.g. applying for jobs on Upwork, polishing your profile, cold DMs, etc.
2. 10 hours working on a project using for a marketing agency client
3. 5 hours finishing a project to customize an existing Power BI reporting setup for a manufacturing company
4. 5 hours creating Tableau dashboards for a non-profit client
5. 10 hours setting up a Google Apps Script to pull data from an API into Google Sheets for a car dealership client
etc.
As you can see, in less than 40 hours in a week, you’ve learned technical skills and “soft” skills, you got your hands dirty building stuff, and maybe most importantly, you’ve gotten exposure to how data and the business looks across 4 completely different domains.
Compare this to what you do in a full-time job in 40 hours – sure you may get to use different tools, but your exposure to different domains and functions in the company will be much narrower.
Overall, freelancing offers tremendous opportunities to learn and grow as a data professional.
Conclusion
Freelancing is a great path if you’re a data analyst, data scientist or data engineer. I’d definitely recommend it if you’re already skilled in data and looking to earn some extra cash or looking to start your own thing.
