The Unsung Data Heroes
How a few data heroes make the wheels spin at high growth companies and why you can’t afford to lose them
There’s a good reason why many of the best data people want to work at high growth technology companies. The learning curve is steep, you can progress quickly, the company’s mission is exciting, and you get to work with the latest technologies.
But working at high growth companies also means that everything changes all the time. Constant changes to the business means that the data expectations have to change too. The median tenure at startups is just two years and there are always a lot of new joiners.
This creates the perfect environment for the data hero.
The data hero is often one of the longest standing members of the data team. It’s that person who still understands the fct_orders.sql table with 500 upstream dependencies and the only one courageous enough to dare make a change.
If you run a data team you know these are the people you can always count on to save the day when things are about to go really bad. They fix stuff faster than everyone else, if they don’t know something they always figure it out, and they take a personal responsibility for the consequence of what happens to the business if data is wrong.
The problem with data heroes is that they hide issues in your team that would have otherwise been exposed and if they leave they’re hard to replace.
Unfortunately, managers rely on data heroes for too much of the mundane work such as fixing urgent issues because they know it will get done. This work is not glamorous and around the two-year mark data heroes too often get fed up and start looking around for jobs elsewhere.
The missed opportunity to companies from data heroes leaving sooner than they would otherwise have is massive.
It takes a while to hit your stride in a new data role. You have to get to know the business, understand existing data models and build relationships with the right people. I’d expect the average data hire to be twice as productive in their second year. Still, the median tenure at startups is just two years.
If you can make your best people stay for four years instead of two you’re getting twice the productivity for the same price.
So, how do you retain your data heroes? Let’s start with what not to do
- Don’t give them all the mundane work just because they know how to do it. Don’t let it be on them to fix it every time your “model_with_500_dependencies_ that_ nobody_ knows.sql” fails.
- Don’t let them be the ones that always have to log in on weekends just because they deeply care about the consequences to the business of data being wrong.
Instead, build the right behaviours into your data culture.
Creating a culture that retains data heroes
Make caring about important data everyone’s responsibility by design. Have a setup where the right people are notified of the data issues they should care about so it doesn’t fall on the same few people each time.
Reward behaviours where data heroes share best practices or decouple the largest data models so more people can contribute.
Make sure that there’s a progression path for individual contributors (ICs) that give your best people room to progress without going into management. We’re starting to see the appearance of staff data roles but they are still uncommon compared to engineering.
In the example above, if you take the happy path you’ll have a data team that’s twice as productive.
Take good care of your data heroes and don’t forget to make room for new ones.
Any thoughts or feedback on the topic? Let me know!
