Table of Contents
- • No silver bullets: Building the analytics flywheel
- • Identity Crisis: Navigating the Modern Data Organization
- • Scaling Knowledge > Scaling Bodies: Why dbt Labs is making the bet on a data literate organization
- • Down with 'data science'
- • Refactor your hiring process: a framework
- • Beyond the Box: Stop relying on your Black co-worker to help you build a diverse team
- • To All The Data Managers We've Loved Before
- • From Diverse "Humans of Data" to Data Dream "Teams"
- • From 100 spreadsheets to 100 data analysts: the story of dbt at Slido
- • New Data Role on the Block: Revenue Analytics
- • Data Paradox of the Growth-Stage Startup
- • Share. Empower. Repeat. Come learn about how to become a Meetup Organizer!
- • Keynote: How big is this wave?
- • Analytics Engineering Everywhere: Why in the Next Five Years Every Organization Will Adopt Analytics Engineering
- • The Future of Analytics is Polyglot
- • The modern data experience
- • Don't hire a data engineer...yet
- • Keynote: The Metrics System
- • This is just the beginning
- • The Future of Data Analytics
- • Coalesce After Party with Catalog & Cocktails
- • The Operational Data Warehouse: Reverse ETL, CDPs, and the future of data activation
- • Built It Once & Build It Right: Prototyping for Data Teams
- • Inclusive Design and dbt
- • Analytics Engineering for storytellers
- • When to ask for help: Modern advice for working with consultants in data and analytics
- • Smaller Black Boxes: Towards Modular Data Products
- • Optimizing query run time with materialization schedules
- • How dbt Enables Systems Engineering in Analytics
- • Operationalizing Column-Name Contracts with dbtplyr
- • Building On Top of dbt: Managing External Dependencies
- • Data as Engineering
- • Automating Ambiguity: Managing dynamic source data using dbt macros
- • Building a metadata ecosystem with dbt
- • Modeling event data at scale
- • Introducing the activity schema: data modeling with a single table
- • dbt in a data mesh world
- • Sharing the knowledge - joining dbt and "the Business" using Tāngata
- • Eat the data you have: Tracking core events in a cookieless world
- • Getting Meta About Metadata: Building Trustworthy Data Products Backed by dbt
- • Batch to Streaming in One Easy Step
- • dbt 101: Stories from real-life data practitioners + a live look at dbt
- • The Modern Data Stack: How Fivetran Operationalizes Data Transformations
- • Implementing and scaling dbt Core without engineers
- • dbt Core v1.0 Reveal ✨
- • Data Analytics in a Snowflake world
- • Firebolt Deep Dive - Next generation performance with dbt
- • The Endpoints are the Beginning: Using the dbt Cloud API to build a culture of data awareness
- • dbt, Notebooks and the modern data experience
- • You don’t need another database: A conversation with Reynold Xin (Databricks) and Drew Banin (dbt Labs)
- • Git for the rest of us
- • How to build a mature dbt project from scratch
- • Tailoring dbt's incremental_strategy to Artsy's data needs
- • Observability within dbt
- • The Call is Coming from Inside the Warehouse: Surviving Schema Changes with Automation
- • So You Think You Can DAG: Supporting data scientists with dbt packages
- • How to Prepare Data for a Product Analytics Platform
- • dbt for Financial Services: How to boost returns on your SQL pipelines using dbt, Databricks, and Delta Lake
- • Stay Calm and Query on: Root Cause Analysis for Your Data Pipelines
- • Upskilling from an Insights Analyst to an Analytics Engineer
- • Building an Open Source Data Stack
- • Trials and Tribulations of Incremental Models
To All The Data Managers We've Loved Before
A talk oriented towards data team managers (current, future, aspiring).
Here, we’ll read aloud our “love letters” to our past and present managers, explaining what makes somebody a great manager, and what doesn’t. The outcome is that people attending this talk will learn a suite of management approaches and tips that result in happy, healthy, and successful data teams.
Follow along in the slides here.
Browse this talk’s Slack archives #
The day-of-talk conversation is archived here in dbt Community Slack.
Not a member of the dbt Community yet? You can join here to view the Coalesce chat archives.
Full transcript #
[00:00:00] Kyle Coapman: Hey, everyone. Welcome to Coalesce. Hopefully you’ve been checking out sessions all week. My name is Kyle and I’m the head of training over at dbt Labs. And I’m super stoked to introduce you to this session, To all the data managers I’ve loved before. I’ll be joined by Adam and Paige from Netlify. Adam is a senior analytics engineer at Netlify and his favorite romcoms are Groundhog Day, Dave, and Shakespeare in Love, and Paige is a staff data analyst and notified her for this talk, and in case you’re not familiar with how our simultaneous chatting happens during Coalesce, make sure you check out #coalesce-data-managers in dbt Slack.
If you haven’t joined dbt Slack, yet be sure to head over to community.getdbt.com, make an account, sign in, and then join the channel #coalesce-data-managers. We encourage you to react with all the emojis, post all the memes, and certainly ask questions of our speakers. Just make sure to use that red question mark so that I can see it, and I can make sureure we pin that as well as Erica, who’s [00:01:00] running the chat for us. After the session, Adam and Paige will be available for 30 minutes answering questions directly in Slack. So be sure to catch up there after the talk and without further ado, let’s hand it over to Adam and Paige. Take it away.
[00:01:18] Paige Berry: Thank you very much, Kyle.
Hi everyone. We are super excited to be here today as individual contributors, talking about our experiences of data team managers that we’ve loved working with and why we loved working with them. So yes, the dbt Slack channel for this talk is Coalesce Data Managers, and we’ll be in there afterwards to continue the conversation.
So now I will turn it over to Adam to do introductions.
[00:01:48] Adam Stone: Hello everyone. My name is Adam Stone. My pronouns are he and him. I am a senior data analytics engineer at Netlify and I will hand off to Paige.[00:02:00]
Hi, I’m Paige Berry, my pronouns are she and
[00:02:04] Paige Berry: her, and I’m a staff data analyst at Netlify on the team with Adam.
[00:02:14] Adam Stone: So together we are on the data team at Netlify and we are 10 folks spread across 10 time zones. And if you saw Emily’s talk from Tuesday, we have three out of the four roles that she mentioned in her talk. We don’t have any data scientists, but we do have a data evangelists.
[00:02:40] Netlify data team mission #
[00:02:40] Paige Berry: Yes, and this is our Netlify data team mission. The data team exists to empower the entire organization to make the best decisions possible by providing accurate, timely, and useful insights, because data is only valuable when it improves your decisions. Also, check out [00:03:00] our blog and careers page.
We’re looking for a new director of data and insights.
[00:03:09] Adam Stone: So together, Paige and I have almost two decades worth of combined experience as individual contributors. From small startups, all the way up to big tech. And throughout all of that experience, we’ve learned that there are specific characteristics of a good manager regardless of the size of the team, or the country.
[00:03:32] Paige Berry: Yeah, we think there’s a lot of information out there on what makes a good manager, but maybe not so much that addresses the unique things about being a good data team manager. And that’s what this talk is here for: it to help fill in that gap.
[00:03:53] Adam Stone: So together, we have figured out four different takeaways for our talk, [00:04:00] or four different categories of takeaways. So expectations, impact on the organization, engagement with the profession, and people over process. Yeah, there are specific things managers can do to make them particularly effective at managing data teams.
[00:04:16] Paige Berry: And this is not something that you have to be just naturally good at. You can learn these things and practice them and become good at them. And you can start doing any of them today.
So how we’ll approach our presentation is that one of us will read a love letter to our data team managers, present or past, and the other person will elaborate on that letter should be fun.
[00:04:44] Love letter 1: expectations #
[00:04:44] Paige Berry: Oh, our first letter, dear data team manager, you clearly communicate expectations. Define what success looks like, and draw strong boundaries [00:05:00] with business partners about what is, and isn’t possible.
[00:05:09] Adam Stone: Communicating expectations is so important. How can a person succeed and thrive if they don’t know what their output or their behavior is expected from them? This manager was absolutely amazing for Paige for several different reasons. One of them being that, that manager knew how to say no to other people, but also did it in a public way, which modeled to the other independent contributors how to say no. Data teams are constantly inundated with requests a fix for a metric or figuring out why a number is up or down, or building a new table, or what have you. Someone is always giving us some more requests, and data and managers have to become very [00:06:00] skilled at saying no in a way that it explains why they’re saying no: maybe not a good match with the company priorities, or maybe not part of what we’re doing at this point in time.
It’s just, Or maybe we just don’t have time for it. So they have to be able to close out the conversation, but leave it open enough to be able to come back and touch on it if needed, and one of the keys to doing that is being able to allow the independent contributors to see it so that they can develop that skill for themselves as well.
[00:06:37] Paige Berry: Absolutely. It really helps to have a good example for this. This manager also created a guideline for like, percent of work time in each work type bucket that really helped clarify that certain things like professional development, proactive insights, cleaning up tech debt really did deserve our time, and we’re part of the value that we bring.
For example, seeing that service [00:07:00] worker number fetching as something to spend to 20% of our time on, an equal percentage is developing and surfacing proactive insights really made it easier to prioritize those number fetching requests into their proper place. And when this manager created and shared the guideline document, several of us on the team independently printed it out and hung it up where we could see it. It was that helpful.
[00:07:26] Adam Stone: Absolutely. Another good thing that data team manager did is to reframe work from being circular work to linear work that we get requests to work on something, and often that request will be open-ended and then there’s always clarification. And so we continue to work on analyzing something and pass it off to the stakeholders, and then the stakeholders will ask another question, and then it becomes this big back and forth. And at the end of the day, No one’s really happy [00:08:00] because the ticket hasn’t been closed. Things haven’t been shipped and nothing’s actually been accomplished. So being able to capture those situations and encouraging the independent contributors to reframe that so that there is an outcome and a conclusion, and that’s clearly communicated ahead of time so that you can feel like you’ve actually completed it and shipped it and moved on to the next thing.
[00:08:28] Love letter 2: impact on org #
[00:08:28] Paige Berry: So our second love letter, Dear data team manager, you help us feel that our data work is driving impact. Knowing that our work is having an impact makes it feel meaningful. And we’ve learned that meaningful work and job satisfaction are linked and knowing that what we do matters can also help with motivation.
So this manager was great, Adam. They really encouraged Adam and the team to [00:09:00] spend time developing and sharing out proactive insights, and that continuous encouragement was really valuable because as an individual contributor, it can be hard at first to set aside tasks and project work requested by others and spend time looking into data that you, the data team member are interested in and think is valuable. But the team found that these proactive insights generated so much conversation amongst the company, so many opportunities to learn from each other and grow the data literacy of the whole company, and it became easier to make time for them every week.
And the impact of them was so clearly visible and positive. Sharing this proactive insight creates political capital for the data team because people learn from them and they love them and they want more, and that increased political capital helps you, the data team manager when you have to say no to someone or ask for more resources for the team, it’s like a big ball of win.
And [00:10:00] when
[00:10:00] Adam Stone: you provide those insights or other amazing data, managers are looking for opportunities to share those things. So when you’re working on something, it can’t really have impact if no one sees it. So you want a manager to look for the right space to be able to post your product, right? So maybe in a Slack channel or as a general announcement channel, or maybe an internal blog, whatever you have at your company for sharing general announcements, something along those.
You want to encourage the independent contributors on your team to continue to post their products in those areas so that other people can learn from those insights. And that will increase the impact over the entire organization.
[00:10:55] Paige Berry: So true and related to that, this [00:11:00] manager encouraged Adam and the team to monitor the different channels and areas where communication was taking place and to just jump in, if they had something to contribute, like an interesting data point or some additional context around a situation that they knew about from their own explorations of the data, and this helped raise the profile of the data team, especially with folks that maybe weren’t as likely to interact with them directly.
And it showed another aspect of how the data team can bring value to conversations throughout the organization.
[00:11:38] Love letter 3: engagement with profession #
[00:11:38] Paige Berry: Dear data team manager, you encourage us to develop our opinions as data professionals to participate fully in the data community, and you remind us that our experiences and contributions are valuable.
Both [00:12:00] of
[00:12:02] Adam Stone: us believe strongly that data workers are happier data workers when they feel involved and able to contribute to their professional community. This manager was awesome for Paige and her team for several reasons. The ones that you see here on this slide. This manager set up shared learning time.
So for example, related to topics of professional development. Like we talked about, setting aside time for professional development and upskilling, and maybe taking a course or doing some reading, but oftentimes that feels really hard when you’ve got a pile of work and you feel like maybe professional development isn’t real work or not part of your job, but guess what?
You’re actually paid to learn those things as well. So you’re being paid to up-skill. So a [00:13:00] manager, a good data team manager will work with an independent contributor to be able to carve out time for professional development and with shared learning time. We were able to set up an hour of time to come together. Every two weeks, we had a shared document where each person put down their learning goals for the coming two weeks. Maybe it would be like, I will read the first chapter of this book, or I will watch this webinar. And then in two weeks time, we would all meet again and review the learning goals. And then we would set up new goals for the next two weeks and that killed two birds with one stone.
It set up a shared learning accountability. And also it means that you’re standing up and saying "I’m going to do this," but maybe you later say, oh, I didn’t do that. But [00:14:00] maybe you do say that you did it. So it increases motivation and it allows everyone on the team to be able to see what everybody else is learning and working on.
So for example, you’re like, maybe a coworker is reading an interesting book and maybe I would like to read that book as well, or maybe we can read it together. So it improves the cohesion of your team as well. Which is just great stuff.
[00:14:24] Paige Berry: Yeah,it totally is .Shared learning hours is one of my favorite things and providing a variety of ways to learn and engage really helps too. This manager created a public channel in our company Slack called data reads. We were encouraged to post and share links to articles, blog posts, talkcordings, Twitter threads that we found that were about like broader subjects impacting the data profession as a whole, and then we incorporated discussing one of these articles and recorded talks once a week in our weekly team meetings, and the benefit of this [00:15:00] was it gave us a chance to think about these broader topics and how they impact the data community. And it also gave us an opportunity to develop responses to the ideas and our own opinions about this stuff. And then when we talked about it, it gave us practice sharing those ideas and opinions with others, which is really good practice.
[00:15:23] Adam Stone: And when you develop those opinions, you might start presenting them. The next example is actually this talk. It just doesn’t get better than this. Here we are presenting to our entire analytics engineering community. We’re connecting with each one of you and after the talk,, we will also be continuing to connect with you in our Slack channel.
It really truly does not get better than this.
[00:15:52] Paige Berry: Yeah, so true. This is awesome. And also asking in one-on-ones what sorts of ways [00:16:00] we’d like to engage is another thing that this manager did. So they asked you, "hey, you want to write a blog post or do a podcast? Present at a conference? Create a tutorial? Have a one-on-one with an industry thought leader?
And then when we’d figure it out, how each of us wanted to engage and talked with this manager about it, they’ve been worked with their own industry network to connect us to those people and opportunities. What an amazing thing.
[00:16:31] Love letter 4: people over process #
[00:16:31] Adam Stone: Whew, our next love letter: dear data manager, you focus on people over processes or tickets. You care about our mental health and make sure that we are taking enough time away from work and consider how we want feedback to be delivered.
[00:16:51] Paige Berry: Yes, this one’s maybe a little more universal for good managers, but when people know that their manager cares about them as a person, [00:17:00] it makes a huge difference in being able to feel psychologically safe at work, which is another really big part of job satisfaction. And this manager was amazing for Adam because they made trusting each other the core value of the data team and that manifested in a few different ways. For example, the idea that anyone should be able to review anyone else’s code, not just seniors reviewing juniors code, but anyone to be able to know enough and be able to review anyone else’s code, and also doing code reviews, with
the the default is "let’s ship it. Let’s get it out there into the world and see what happens." We also did things as a team that fostered the feeling that we’ve got each other’s backs. That’s going to be really important on a data team, because sometimes we have to do things that can feel a little scary. Correcting a leader in a meeting when they’re misinterpreting the data or hey, talking about how you maybe had the data wrong in something or made some assumptions that weren’t right.
[00:18:00] And those things can be scary to talk about or to do in the midst of a big company, but when you’ve got a team behind you that has your back, it makes it a little easier to do those kinds of somewhat scary things.
[00:18:18] Adam Stone: One thing that we have done again and again, is to watch a YouTube video by Andreas Klinger called "managing remote engineering teams at scale." We’ll drop that link in the Slack channel right now. What’s up with that video?
[00:18:36] Paige Berry: I really loved that video where we talking about- w single-player mode, actually, because it talks about how you can build trust on a team and, make it so that we don’t so that we unblock each other.
We’re able to trust each other’s work and work independently. If you’re on a team where you’ve [00:19:00] got people in all sorts of time zones, it’s really nice to not have to wait for someone else to come online to get your work done. Some really good stuff in that video.
[00:19:13] Adam Stone: And that promotes an asynchronous work culture, which is really valuable. It’s valuable for bringing your whole self to work, whichever buzz phrase right now that we’re hearing more and more in the industry, but what does that really mean? It means that you’re able to bring your entire self to work your self as a human, as a family member, as a partner.
And whatever else is happening in the background of your life, you’re able to bring that self to work. And oftentimes people say that phrase, but the space isn’t really there at work for them to be able to bring their whole self, but your whole self is a lot, right? There’s a lot to bring to the table. [00:20:00] So promoting an async work culture reduces the emphasis on being there at a specific time, or always reporting within five minutes or responding within five minutes. And it allows you to build a team work culture that is more focused on the async work, that frees up more space for people to bring their whole selves to work.
[00:20:29] Paige Berry: Absolutely. Another way people are different is we like to receive feedback differently. So this manager asked the team how each person wanted to receive feedback. And then delivered feedback, according to each person’s preference. This may sound like a small thing, but in reality, it’s huge to be asked a question like that.
And then to have the manager be responsive to the answers really made the team feel seen and valued as individually as people.[00:21:00]
[00:21:00] Adam Stone: Which is actually related to PTO. PTO is a weird thing that people care a lot about, but they don’t really like to talk to openly about it. Or maybe they’re not exactly sure. It’s a gray area, what the actual policy is, and what the manager actually thinks about it. And it’s worse if you work at a company that has unlimited PTO, like what does that even mean?
How much is too much? So a good day to team manager will focus on helping clean up that ambiguity and make the whole thing more transparent for everyone on the team. So this can happen in different ways. Perhaps the manager is communicating expectations for how much PTO someone should take monthly or annually.
It can be something simple. So we’ve got this policy and that translates into about two days off per month, plus two weeks of [00:22:00] uninterrupted vacation annually, and communicating that to the whole team so that everyone is on the same page. Another good thing that you can do is to have a leaderboard where you’re actually putting the days off that people have taken out there so people can publicly see it. And that list of ranking people means that the people on the bottom will feel like, oh gosh, I’m not taking enough time off. I’m not putting in enough requests. I want to catch up to the folks at the top of the leaderboard.
And that increases the transparency around the entire topic for the whole team.
[00:22:40] Takeaways #
[00:22:40] Paige Berry: Yes. So there are specific things that data managers can do to make them particularly good at managing data teams, learn those and you shall succeed, and these things can be fit into four categories, managing expectations, impact on the organization, engagement with the profession, [00:23:00] and people over process. So you can start thinking about one or two of these for now. You can start doing really any of these today. Start with something small at first and go from there. So we’re going to give you a few examples of some concrete things you can start doing today, either as a data team manager currently managing a data team or aspiring data team managers, or even as individual contributors who are looking to make things better for yourself, for your teammates to do a little managing up. So these tips and moral be shared in our Slack channel too.
[00:23:38] Adam Stone: So as a data team manager or an aspiring data team manager, these are things that you can do right now after this talk: set up work buckets for the type of work that your team is expected to do and put in those percentages for how much time they’re supposed to spend on those things. And then check in with the team to see what they say or what they think.[00:24:00]
Another thing that you can do really quickly is setting up a data reads channel, and just post a variety of articles from your professional community. And makes sure that anyone can post in that channel and you can invite people outside of your data team who like to talk about data or who are interested in that particular content and communicate the expectation that it’s not a required reading list. You’re not required to respond to every post that’s in there. It’s just a live feed. You can jump in, jump out whenever you want [and] start the discussion at any time.
And another thing you can do today is related to PTO, increasing the transparency around PTO on your team, communicating expectations about how much PTO should be taken, [00:25:00] and having a running leader board so that we all know where everyone stands in relationship to PTO.
[00:25:07] Paige Berry: As an individual contributor, you can think about how it works best for you to receive feedback, and then you can let your manager know. This helps your manager, who likely wants to make sure that the feedback is landing with you. It helps you for sure. And it might even help your teammates too, because your manager may then think, I wonder how other people on the team would prefer to receive feedback.
And you could watch this video, excuse me. The video we talked about on building trust on remote engineering teams, and think about how it applies to your team. You might even suggest that the whole team watches it and has a discussion afterwards.
And you can think about how you might want to connect with the data analytics professional community. Write a blog post, guest on a [00:26:00] podcast, present at a conference. I heard there’s this really cool one called Coalesce, mentor an early career data person, and then you can discuss with your manager how to make this happen. As a bonus, your manager then may think:
"I wonder how other people on the team would like to connect with the data professional community."
[00:26:24] Adam Stone: Again, thank you so much for coming to our talk. Much love to all you guys. You are an amazing community. And think about one of the key takeaways from this talk, which is that you can learn to become a great data team manager. It’s not something that you’re born with are specific skills that you can learn and be amazing at that.
[00:26:50] Paige Berry: Yeah. And then perhaps someday the folks on your team will be writing love letters to you.
Last modified on: Oct 13, 2022