Data org structure examples

The data team is a brand new thing: it’s not “IT”, it’s not finance, it’s not any of the typical business functions within an operating business. So…who does it report to? How does it interact with the rest of the organization? How big is it?

These are all questions that are getting answered in real-time throughout the industry. And they’re likely questions that you have as you go about constructing, or re-architecting, your data team. As of today, there are no clear answers. Companies are answering these questions in a bunch of different ways, all customized to their particular businesses.

This is why this question is such a hard one. So rather than opine about what we think the best answers are—and we do have our own opinions!—we figured it would be most useful to collect a bunch of “reference architectures” from amazing companies. Peruse them and see if any of them resonate with the team you’re trying to build. The core themes we picked out that seem to drive the design of a modern data team are:

• How centralized / how distributed? Some teams are highly centralized, some teams distribute members to sit and work with organizational units.
• Where should data engineering sit? Some teams put data engineers on the data team, some draw a dotted line wth the engineering organization.
• What is the role of the data team? Some teams embrace data as a product, and some teams operationalize data as a service. (More on this topic here.)
• What executive does the data team live under? Some teams have VP- or C-level executives leading them who report directly to the CEO. Other times data rolls up to a functional head.

A final note before we dive in: all of the leaders who are profiled below are members of dbt Slack and have kindly volunteered to answer specific questions on this topic. If you’re going through this process right now, reach out to them on Slack. If you’re not already in the community, now’s a good time to jump in!

Away Travel #

Mike Berardo

Director Data & Strategy

Industry: Ecommerce

Company Size: 250 employees

The numbers

⚡️3% of the organization focused on analytics

🔎20% of the organization is comfortable using BI tool

🔬1 data scientist (provisions and analyzes data)

⚙️1 data engineer (provisions data)

📊5 analysts (mostly analyze)

Data team org structure

Away’s data needs are supported by five people on the analytics team, and one person on the data science team, both teams report to the Director of Data & Strategy. The one-person data engineering team works closely with the Data & Strategy team, but reports into engineering. Data & Strategy reports to the CEO, though Mike points out that this is an interim setup, long-term, data will report to the CFO.

The expectation at Away is that business stakeholders can do their own analysis, though customer experience, legal, and people operations all have dedicated analyst support.

Growth plans

“We just hired three people,” Mike says, “So we’re getting closer, but realistically, I think we still need more. In particular, we need an analytics engineer. The gap between data engineering and analytics is getting too wide and we really need a technical resource that sits with analytics to help us bridge that gap.”

HubSpot #

Gordon Wong

VP of Business Data

Industry: SaaS

Company Size: ~3000

The numbers

⚡️5% of the organization focused on analytics

🔎10% of the organization is comfortable using BI tool

⚙️10 data provisioners

📊120 data analyzers

Data team org structure

As the VP of Business Data, Gordon manages data engineering, data warehousing, and analytics enablement. Like many SaaS businesses, HubSpot’s software creates and moves an enormous amount of customer data, which can be challenging to understand. At the same time, data engineering is seen as a skill distinct from full stack engineering. Because of this, the data engineering team is staffed by skilled software engineers but reports into business Intelligence. The data engineering cluster on Gordon’s team is focused on building data infrastructure that supports business and product analytics.

Gordon describes his team today as “engineer heavy.” They are focused exclusively on enabling analytics, not doing analytics. The analytics function is fully decentralized with each business function hiring its own analysts and data scientists.

Growth plans

Gordon feels that his own team is not badly understaffed for data engineers but needs a lot more enablers. He feels the biggest constraint on delivering more business insight is not data engineering but rather “the lack of analytics enablers to bridge the gap from atomic data to usable information”. He plans to focus on adding analytics engineers, as well as “analysts who can train the analysts”.

M.M. LaFleur #

Kailin Lu

Analytics Manager

Industry: Ecommerce

Company Size: 100 employees

Important ratios

⚡️2% of the organization focused on analytics

🔎30% of the organization is comfortable using BI tool

⚙️📊2 analysts who are responsible for both provisioning data and analyzing data

Data team org structure

“We are a small but mighty team of two,” Kailin says. “We use Stitch to pipe raw data into our warehouse, and dbt to maintain our data models and ensure we have clean and updated data to work off of.” One analyst focuses primarily on supporting the marketing team and the other focuses on supporting sales and inventory. Both analysts report to the Chief Product Officer.

In addition to the analysts, M.M. LaFleur has about five business users who Kailin says are power users, and are able to develop their own reports on an ad hoc basis.

Yerdle Recommerce #

Caitlin Moorman

Head of Analytics

Industry: Retail

Company Size: 75

The numbers

⚡️5% of the organization focused on analytics

🔎40% of the organization is comfortable using BI tool

⚙️~1 data provisioner

📊2 data analyzers

Data team org structure

Yerdle Recommerce is currently undergoing a reorganization from a fully centralized approach to a more hybrid structure. “The fully centralized model doesn’t work!” Caitlin says emphatically. “Right now we have a service provider model for analytics, but skill sets are strongly divided between ETL and visualization. The data team attempts to serve all teams within the organization, but we’ve got a huge backlog and disconnects between analytics and functional teams.”

In the hybrid model, Yerdle’s data team will have a team lead and a data engineer acting as centralized resources. Data analysts and data scientists will have strong dotted lines to operations, account management, and product teams, but will be part of the central team. “This approach has worked well for me in the past, “Caitlin says. “It helps analysts feel like they have the support they need in terms of tools, methods, and best practices, but allows them the bandwidth to become subject matter experts.”

Growth plans

Caitlin currently has three analyst roles open on her team. “Once I fill the outstanding openings, we’ll be in a pretty good place to scale with the company.” Her plan is to have one analyst per line of business, but adds, “We’re early-stage, so we’re still figuring out how much ongoing data support teams need.”

GetYourGuide #

Baran Toppare

Data Analyst

Industry: Consumer services

Company Size: 600 employees

The numbers

⚡️2.50% of the organization focused on analytics

🔎42% of the organization is comfortable using BI tool

🔬4 data scientists (analyze data)

⚙️10 data engineers (provision data)

📊6 data analysts (analyze data)

Data team org structure

“Each analyst is embedded into 1-2 mission teams,” Baran says. “We support product, supply, marketing, and finance. We all report to our Director of Analytics.” For the most part, data scientists are not doing analytics, focusing instead on machine learning projects on the product roadmap.

15Gifts #

Peter Fine

Senior Data Scientist

Industry: SaaS

Company Size: 45

The numbers

⚡️9% of the organization focused on analytics

🔎100% of the organization is comfortable using BI tool

🔬3 data scientists

📊5 data analyst

Data team org structure

15Gifts’ data team includes three data scientists and five analysts, all reporting to the head of data.

Growth plans

Peter doesn’t feel that the team is staffed quite right today, but adds, “We are currently hiring a dedicated data engineer to manage data infrastructure performance.”

Mack Weldon #

Charley Moore

VP, Finance & Operations

Industry: Ecommerce

Company Size: 35 employees

The numbers

⚡️3% of the organization focused on analytics

🔎30% of the organization is comfortable using BI tool

🔬1 data scientist ( analyzes data)

⚙️Analytics engineering consultant (provisions data)

Data team org structure

Today, Mack Weldon’s data team consists of one data scientist and consulting support from Fishtown Analytics. When a team needs a new type of analysis, they do it themselves using Looker to query data that has been cleaned and prepared with dbt.

This setup has been good enough to get them where they are today, but Charley has plans to expand internal analytics capabilities quickly, “I would like our team to be at 3-4 people by the middle of next year.” This plan comes from internal thinking around how Mack Weldon wants to structure the data team over the long-term. They settled on a centralized analytics team with specialized analysts (the first of these roles is currently open here).

This centralized team will continue to report into Finance and Operations. “We wanted our data team to be cross-functional and to live above the influence of any single business unit,” Charley said. “We wanted to centralize all of our data tools and have a single source of truth for data that flowed from our data team to other teams.”

Thanks to all of the teams who shared their experiences! If your team’s org structure is radically different than any of these, we’d love to hear about it on dbt Slack.

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