Being a Data Coach

Problem

Two years ago, I went for a vacation, which allowed me to step back and reflect on what I wanted to do in my life. At that time, I was a manager at a company that was one of the main Uber/Lyft competitors in Europe. Gradually, I started to feel the constraints of a permanent position and wanted to be able to decide on what I should be working on. When I returned from my vacation, I talked to my boss, and they helped me end my contract in the most favorable way.  

I entered the arena of freelance coaching with seven years of experience under my belt, working with a number of clients and companies and being well versed in different technologies and architectures. I could tell that my versatile expertise will particularly benefit smaller companies from 50 to 500 people and help them make the best data platform decision. I started my own company in August and had my first client in September last year. Things simply snowballed from there.

My main concern in a new role was how I should position myself within a data team I was coaching given my part-time, freelancing role. How, as an individual, can I bring value to a team of ten people being involved for two days per week? How can I demonstrate my usefulness to the team, given that my services are not inexpensive?

Actions taken

For starters, I reflected on my prospective freelance engagement in the data space. Most freelancers do more operational than coaching things, at least in Europe. What I wanted to do -- and where my strengths were -- was to help clients improve their skills and expand their expertise rather than me doing work instead of them. I am a one-man enterprise that brings in skills and expertise when people need them.

I would typically start my engagement by interviewing everyone on the team along with some external partners to better understand how they interact among themselves. I would also want to learn about their problems, likes, or competencies as well as how I could best communicate with them. Sometimes people would be open about troubling issues; other times, I would have to detect them. Also, from the very beginning, I would want to reassure people that they could count on me. Even if I was not working at the moment for a particular company, I would want people to contact me if they encounter an issue. Also, I would be quite responsive to demonstrate that my expertise could save them time and thus position myself as an expert.

To showcase my expertise, I would have to host knowledge-sharing sessions. I love sharing knowledge, and I enjoyed doing it when I was a manager too. I called those sessions kata -- everyone would be working on the same topic, and I would help them learn new things collectively. I would help define a topic beforehand and then facilitate the learning session. I find this approach highly beneficial for my coaching, and I try to introduce it to any new team.

I never regretted choosing to freelance. In France, and throughout Europe, there is a lack of data engineers. If one needs to employ for a permanent position, they will have to look for the right hire for 3 to 6 months at least, if not more. As a freelancer, I am constantly available. I see myself as someone who can fix up a problem without much commitment. Also, when companies need someone senior, they want to see the value they bring fast. If that person cannot deliver the value, it’s much easier to end the cooperation with a freelancer than with someone employed in a full-time role. Flexibility is the main reason why companies are choosing freelancers over permanent staff. Also, in France, there is a huge gap in demand and supply. There are many open positions with no one to fill them. In recent years, everything around data has become insanely popular -- data is all around, and everyone would like to do something with all that data. And to do stuff with data, one needs data engineers. That was one of the reasons that helped me decide to go freelancing with ease; I knew that I wouldn’t stay jobless for a longer period of time. Not only are people looking for data experts, but in all honesty, they are prepared to pay hefty amounts of money for it.

The only downside is that the market in Europe lags some two years behind the US. All the tools we use are made in the US. In France, we either use their approach as inspiration or copy them A to Z. We also have far fewer people and given the demographics, we should have 10x fewer data experts. We have to be more creative if we want to do the same with fewer resources.

Finally, as a freelancer, I need to be visible at all times. I started a newsletter in English, but I also started to self-produce video content on Twitch and YouTube in French. My main intention is to show that data engineering is not geeky to the audience in French-speaking countries because there is so much more content in English than in French.

Lessons learned

• Franky speaking, I don’t think that my engagement would be much different if I worked full-time for a company. I would still have to demonstrate my competencies and showcase my value. But as a freelancer, I have the freedom to choose what I want to work on and when.
• I am not a super-organized person, but so far, my freelancing is working well. I am managing time well and staying close to a team. The way I see my position is: I am on my own but I want to be part of the team. Even in my freelance role, I like to blend in and be part of the team I am coaching. I want other team members to perceive me as a team member and not someone coming from the outside.
• Just do it. Every time I want to create some kind of content I am overwhelmed by self-doubt knowing that haters on patrol will be there to voice their criticism. Well, if you don’t produce anything, no one will see it, and no one will criticize you. My advice is: do first, think after.