💙 Making your first hire, building trust, mapping company needs, and more
How to build a data team that will grow with you
ICYMI: Last week we released our latest ebook, Secrets of a Modern Data Leader: The First 365 Days Inside a Data Team.
At Atlan, we started out as a data team ourselves, and I sometimes struggled to lead our data team through massive projects and unprecedented challenges. The problem is, unlike other roles and functions, there really isn’t a playbook for how to be a great data leader.
So we created one! To learn what it really takes to lead a data team to success, we brought together insights and learnings from four of today’s best modern data leaders:
Erica Louie: Head of Data at dbt Labs
Gordon Wong: Principal Consultant and Founder of Wong Decision Intelligence; formerly Senior Leader of Business Intelligence at Hubspot and Fitbit.
Stephen Bailey: Data Engineer at Whatnot; formerly Director of Data and Analytics at Immuta
Taylor Murphy: Head of Product and Data at Meltano; formerly Manager of Data and Analytics at GitLab
Today, in Metadata Weekly, we’re sharing an excerpt from Chapter 2 of this ebook — all about building a great data team from your first day. We hope you find their lessons and insights as interesting and helpful as we did.
✨ Spotlight: How to build a data team that will grow with you
So you’ve just joined a company as their data leader. Congrats!
Whether you’re expanding an existing data team or creating one from scratch, building the right team and culture is a huge part of your success as a data leader.
Here are three key team and culture to-dos to nail in your first year as a data leader.
🤔 Determine your company’s data needs
Your first hire can set the tone for your team’s structure, and it says a lot about what the data team is prioritizing. You’ll know what a good first hire will look like once you figure out the immediate data needs of your new workplace.
“It does depend on your technical requirements,” said Stephen Bailey. “If your team is working with reams of data, you need someone who can come in and optimize for that — maybe a data engineer.”
“In general, I would say, focus on people who can shape the data in a way that the business can use and really focus on impact first, before thinking about scaling.”
— Stephen Bailey
Part of the beauty of developing your strategy first is being able to look at all the information available to you to make the best decision.
“If your company is already on the modern data stack and is comfortable using some of these SaaS tools, then maybe a data engineer isn’t the first person you hire,” said Taylor Murphy. “Maybe it’s an analytics engineer or an analyst to solve some of those pain points.”
Erica Louie didn’t hire for her first nine months on the job. “It was a lot of collecting problems everyone was facing, a lot of just trying to get our infrastructure in a good place. And then trying to build relationships with all the department heads to then understand what data needs they actually had.”
Remember, if your colleagues aren’t data experts, they may not understand their own data needs, but this is where the listening phase serves you well.
Does the product team have the highest needs in the company? Does the marketing team need data to build an audience? The answers to these questions will show who to hire first.
“A lot of hiring is culture, and a lot of it is understanding power structures within existing organizations.”
— Taylor Murphy
✅ Action point: Create a map of needs
Prioritizing tasks will help you map out your to-do list and get staff excited about what they’ll be working on. Here are some questions you might ask your stakeholders:
Where does the company need to be by the end of the year?
What information does each department need in order to get there?
What tools would improve efficiency?
How can the data department bring value to the company right now?
When you have a to-do list, you have a sense of purpose, which makes solving priority issues and working towards larger organizational goals second-nature.
💙 Build psychological safety
One of the key parts of being a modern data leader is working on your leadership skills. Data is a human issue, and the humans you hire to work with you on data deserve a leader who has put in the effort to create a people-focused environment.
What is psychological safety? It’s the ability to ask questions, share ideas, and raise concerns without fear of consequences to your self-image, status, or career. The more psychologically safe your workplace is, the more you’ll see creative conversations and exciting ideas.
“I think that by me being upfront and transparent and vulnerable, it created this really comfortable work environment for us, where everyone is open to say if they don’t know something.”
— Erica Louie
Deciding what kind of employer you want to be is an important part of modern data leadership because you shape your team.
“As I try to manage my people, I tell them this: as a manager, it’s my job to try to understand you, to try to give you honest feedback, and to believe you can achieve your goals,” said Gordon Wong.
At the same time, remember that your team consists of complex people who choose to bring their experiences and expertise to your table.
“I want people to be able to rise to the occasion and learn and grow how they want to. I’m not asking them to do something I wouldn’t do myself. We’re all on the same team here.”
— Taylor Murphy
🤝 Make your first hire
Your first hire often becomes the foundation of your data team and its culture, so who should you hire?
We asked each of our data leaders about their very first person they hired into their data teams. Here’s who they hired and why:
Erica Louie: A Production Analytics Engineer and Marketing Analytics Engineer
“They have the highest data needs out of everyone in the company. It was so important to have a really strong foundational product data person, because that is how we measure impact in our company. And if marketing is doing an initiative, then the thing that they want to measure is how our users are using the product.”
Taylor Murphy: A Data Engineer comfortable with analysis
“I intentionally tried to find somebody who would be comfortable doing some analysis, even if it wasn’t perfect — someone who had spent some time in SQL but was very focused on data engineering. I think that’s good for Meltano, because their target persona is basically data engineers. So the engineer is also kind of dogfooding the product.”
Gordon Wong: A Solution Architect
“Look at engineering — you should have a product owner, right? But who’s thinking about product outcomes? This is probably where the opportunities are to have a project manager who’s thinking about efficiency and taking care of the team. You should have that Solution Architect because, as a manager, you can’t do all of it.”
Stephen Bailey: Analytics Engineer
“I would start with an Analytics Engineer, someone who can build and manage data models that will serve multiple dashboards. Then I would invest in analytics.”
🎓 Masterclass: How data governance accelerates Contentsquare’s analytics and BI
Don’t miss out on our last masterclass of the year, coming up on December 19!
We’re hosting Contentsquare, the world’s largest dedicated experience analytics platform company, to share their learnings around leveraging collaboration, data quality, and data cataloging for effective data governance. The event will be led by:
Otávio Leite Bastos, Global Data Governance Lead at Contentsquare
Kenza Zanzouri, Data Governance Strategist at Contentsquare
Austin Kronz, Director of Data Strategy at Atlan
Otávio and Kenza will talk about how the Contentsquare team distributes ownership of data products while maintaining a centralized platform where producers and consumers have transparency into their data. Topics will include:
How to prioritize data governance initiatives in a rapidly growing company
How following a data product mindset can enable data discovery across a variety of personas
Best practices on incorporating upstream data sensitivity classifications, data quality checks, and automating alerts for downstream data owners and consumers
Sign up for the masterclass ➡️
📚 More from my reading list
2022 highlights by Towards Data Science
How do we actually “pull stories out of data”? by Randy Au
Managing the abstraction mix by Katie Bauer
The go-to guide for how to work with data people by Benoit Pimpaud
What I learned in my first 6 months as a Director of Data Science by CJ Sullivan
Use the Same Page Tool to get your team aligned by Adam Sroka
See you next week!
P.S. Liked reading this edition of the newsletter? I would love it if you could take a moment and share it with your friends on social.