I hire a lot of #Tableau developers. If you want to set yourself apart, participate in #MakeOverMonday, #WorkoutWednesday, and/or #SportsVizSunday. I guarantee you'll get a good paying job. Also #GoDawgs!— Mark Jackson (@ugamarkj) December 1, 2018
Unfortunately my Dawgs didn't hang on to beat Alabama in the SEC Championship, but I did get a pretty significant response to my career building advice. It sparked quite the debate and led down paths I didn't foresee. In retrospect, I could have eliminated some confusion by revising the guarantee part of the tweet. Language can often fail us as we attempt to communicate clearly. I'm one that generally loves debate and have a lot of experience with being misinterpreted. Our worldviews can generally interfere with the way we interpret the words of others. My best advice is to have grace with people and seek clarification.
Anyway, the correct way to understand my "guarantee" can be summarized by Brian Fantana in "Anchorman":
Everyone has a different learning style, but if you are highly motivated and would rather not spend money when it isn't necessary, then #MakeOverMonday, #WorkoutWednesday and #SportsVizSunday are fantastic programs to engage with. You get exposed to a multitude of ways that data can be visualized. You get practice experimenting with new techniques. You get constructive feedback from the community. And you build a portfolio that you can share with prospective employers. All it requires is your time. No one is going to ask you for money to participate.
The Tableau community is incredibly giving and encouraging. People run these programs in their spare time because they enjoy doing it and want to pay it forward. I've found newcomers to be incredibly welcome in these environments. There are plenty of stories where these programs have had a profound impact on helping people secure great jobs in data.
So why spend thousands of dollars paying for training or traditional education when there is so much free content available? There are literally hundreds of people blogging about Tableau. Most of the active community is on Twitter so it is a great place to source training material. You can also follow along with free training videos on Tableau's own website. Tableau also publishes all the sessions from their conference on their YouTube channel. You can find the TC18 session videos here.
You may be surprised to learn that I got to be good with Tableau by reading the manual. It may be the most boring way to learn, but you can learn the product pretty deeply that way. Participating in the Tableau Forum is also a good way to learn. I spent a little time doing that as well.
Time is scare though so if you need to prioritize, I'd sway you towards the programs mentioned in my tweet. They don't require too much time to build skills, you get to engage with a lot of cool people, and you build a portfolio in the process. If you are shy, you could also participate less publicly. Eventually I'd encourage you to befriend some of your fellow data workers. I promise they are nice and very humble people.
Is a public portfolio the golden ticket to a great paying job? No, but it certainly helps. If you are still having trouble securing a job in spite of all of your skill building efforts. Then there may be other areas you need to work on. Emotional Intelligence (EQ) is extremely important, especially as you try to move up in an organization. Paul Banoub recently published an excellent blog post on this topic. It is a must read. There may be other factors that are outside of your control and every situation is unique, but there is still a lot you can do to increase your odds of landing a great job.
Do I have to work in my spare time and engage in social media to get a good job in data?
This depends on your goals. Everyone's situation is different. But if you want to be one the best at anything, then you have to put in the effort. Top tier athletes don't get to where they are without thousands of hours of practice and continual development. Everything comes with trade-offs. If you aren't allowed the time to really develop your skills at work, then you'll have to trade some of your spare time to reach your goals. I've reached a lot of my career goals at this point, so I've re-balanced my life some towards leisure and family time. I'm still pressing myself to build machine learning skills, but to a lessor extent than when I was brand new to data.I think social media can be both good and bad. It can be a good way to get your name out there and a good way to give back to the community by sharing your skills with others. It is also a great way to network with people in your field. It can be a bad then when you haven't developed your EQ. Look no further than the toxic nature of political discussions on social media these days. Dramatization:
Person A: "It's hot today!"
Person B: "I bet you voted for Trump, so this is all your fault!"
Person A: "WTF?"
My advice to employers of data workers
Give your employees space to grow. I encourage my employees to spend half a day every other week pursuing skills development. In the alternating week we have a 3 hour team meeting to share the things we've learned with the rest of the team.When looking for new candidates do your due diligence. Just because someone has an online portfolio, it doesn't mean they know what they are doing. They could have just copied the work of someone else. You should definitely layer on a technical assessment to determine how good they really are. We model our assessment after the Tableau Certification exam. Honestly, I hire more newcomers than prospects that have years of experience on their resume. I think the newcomers take the exam more seriously. I'm gauging cognitive ability in the exam. So a newcomer that has spent a week with Tableau that does almost as good as someone with three years of Tableau experience is going to land the job all else being equal.
I have three criteria I'm evaluating in the interview process:
- Cultural Fit: Are they genuinely excited about data work? Do they have an attitude of continual learning? Do they want to share their experience with others? Do they have something unique to contribute to the team?
- Technical Experience / Cognitive Ability: Specific technical experience is good, but more importantly: are people quick learners with technical software? This is assessed by some technical questions about SQL, Excel and the Tableau technical assessment. We give people a week to prepare for the technical assessment.
- Industry Experience: For me, this is having a background in healthcare. Even better if they have specific experience with our specific healthcare records system. This is the least important criteria, but sometimes it can be elevated in importance to balance out the team.
Thanks for reading! Feel free to tag me @ugamarkj on Twitter to continue the conversation.
