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• Data Team Organization
  • Data Fusion Team Organization
    • Fusion Team Assignments
    • Manager, Data
    • Data Champion
    • Lead Analytics Engineer
    • Data Platform Team Stable Counterpart
    • Data Fusion Team Operations
• Data Program Recruiting
• Data Roles and Career Development
  • Data Internships
  • Data Platform
    • Intermediate and Senior Data Engineer Onboarding Timeline
  • Data Analyst
    • Intermediate and Senior Data Analyst Onboarding Timeline
  • Data Science
    • Intermediate and Senior Data Scientist Onboarding Timeline
  • Analytics Engineering
    • Analytics Engineering Job Family
    • Intermediate and Senior Analytics Engineer Onboarding Timeline
  • Data Management
    • Data Manager Onboarding Timeline

Data Team Organization

The Data Team Organization model is guided by three primary business needs:

1. The need for bespoke data solutions unique to the GitLab business.
2. The need for high-performance and reliable data storage and compute platform to support distributed analyst teams.
3. The need for centers of excellence for data technologies and advanced analytics.
4. The need for flexible data solutions driven by varying urgency and quality requirements.

Based on these needs, the Data Team is organized in the following way:

1. Data Fusion Teams: Business-Focused and Business-Involved teams responsible for delivering data solutions
2. Data Platform & Engineering Team: Center of Excellence for data technologies, including owning and operating the Data Stack
3. Data Science Team: Center of Excellence for advanced analytics, including delivery of data science projects to the business
4. Data Collaboration Team: Center of Excellence for business intelligence and data findability

Data Fusion Team Organization

Data Fusion Teams are composed of team members from across the business and the Data Team. Read about the current Data Fusion Teams on our front page.

Fusion Team Assignments

The Data Fusion Team has several leadership roles on the team. These leaders live the GitLab Collaboration value and achieve great Results while doing so. The Manager, Data, Data Champion, and Lead Analytics Engineer provide leadership, mentoring, and guidance to the Data Fusion Team.

Manager, Data

In support of the Data Fusion Team, the Manager, Data fulfills the below responsibilities from the Senior Manager, Data Job Responsibilites:

1. Works with the Director, Data and Data Champions to envision and draft Quarterly Objectives, driven by requirements gathered from multiple business partners.
2. Monitor, measure, and improve key aspects of the Data Fusion Teams.
3. Regularly meet with business partners to understand and solve for data needs.
4. Serve as a Maintainer on the Data Team Project. Provide final review, feedback, and approval of Merge Requests submitted by the Data Fusion Teams.
5. Work closely with the Enterprise Data Fusion team to develop and evangelize Dimensional Modeling adoption and best practices.

Data Champion

In support of the Data Fusion Team, the Data Champion is the DRI for Data within a Functional Analytics Team. The Data Champion fulfills the below responsibilities from the Data Champion Program in Data Fusion:

1. Develop a Data Success Plan in coordination with the Data Team.
2. Serve as Data DRI for their functional team, capturing all Data requests, including Data Quality problems.
3. Prioritize and stack-rank Data Issues and Epics, ensuring the Data Issue Board remains up-to-date.
4. Communicate priorities to the Data Team through the Issue Board.
5. Develop a Data handbook page geared to their functional team audience, such as Data For Product Managers.
6. Regularly communicate and cascade data news and practices.
7. Helps to improve Data Quality in source systems.
8. Watch changes in source systems and help incorporate necessary updates in the Enterprise Data Warehouse.

Lead Analytics Engineer

In support of the Data Fusion Team, the Lead Analytics Engineer fulfills the below responsibilities from the Senior Analytics Engineer Job Responsibilites:

1. Own one or more stakeholder relationship in Go To Market, Research & Development, General & Administrative, Financial Analytics, or Engineering Analytics business functions.
2. Co-DRI of Key Results along with the Manager, Data.
3. Lead work breakdown sessions for OKRs. Incorporate the Data Champion and other required team members in the work breakdown sessions.
4. Lead twice-weekly iteration planning sessions for the assigned Fusion Team. Work with Data Champions to prioritze P3-Other issues. The target state is for the Fusion team to spend 75% of their time working on OKR issues and 25% of their time working on Other issues. The OKRs are set by the Manager, Data and the Director, Data. Any changes to these priorities will be coordinated by Data Management.
5. Review the weekly stand-up and provide support as needed to unblock team members and answer questions.
6. Update the Rolly Bot Epic description section for the Key Results that the Lead AE is Co-DRI off.
7. Keep Fusion Team boards updated on a bi-weekly cadence aligning to the Planning Drumbeat cadence.

Data Platform Team Stable Counterpart

Following the GitLab Stable Counterpart principles, every Fusion Team have a Data Platform Team Stable Counterpart assigned. The Data Platform Stable Counterpart divides their time, work and priorities between the Data Platform Team and Fusion Team (general an average of 50% each). The Stable Counterpart is aware of the direction and priorities of the Fusion Team and when needed brought into discussion with the Data Platform Team. I.e. when there is a bigger demand than the Stable Counterpart can handle in the assigned availability or architectural direction needs to change. The Stable Counterpart recognize, flags and address this with the applicable stakeholders (in general the Lead/DRI of the Data Platform Team and the Fusion Team).

The stable counterpart is expected to participate in the following meetings asynchronously or synchronously. When in doubt, please reach out to the Fusion Team Manager to learn which meetings on the calendar you should participate in. In general, the meetings in scope are as follows:

1. Data Team <> Business Function syncs. For example, People Analytics <> Data Team, Engineering Analytics <> Data Team, Product Intelligence <> Data Team syncs.
2. Data Program Sync meetings where issue prioritization is discussed amongst cross-functional stakeholders. For example, Data Program Support for R&D and Data Program Support for GTM.
3. Data Fusion Team Iteration Planning Meetings.
4. Data Fusion Team Meetings.

Data Fusion Team Operations

Critical to successful Data Fusion Teams are the following elements:

1. Regular and transparent Engagement with Business Partners and Data Champions through the Data for GTM Series and Data for R&D Series.
2. Planning our Work through the Data Team Planning Drumbeat
3. Performing a regular CSAT survey to provide feedback to the Data Fusion Team towards the goal of continuous improvement

We encourage our stakeholders to follow along with our issue boards to understand the scope of work:

Data Program Recruiting

Recruiting great people is critical to our success and we've invested much effort into making the process efficient. Here are some reference materials we use:

• a GitLab Data Recruiting video to say "Hi" and give you some insight into how we work and what we work on. Enjoy!
• Data Roles and Career Development to help existing team members and prospects understand growth opportunities
• a Take Home Test that we ask each candidate to complete; this test is good for the candidate and for us because it represents the type of work we perform regularly and if the candidate is not interested in this work it helps them make a more informed decision about their application

Data Roles and Career Development

Data Internships

See Data Team Internships.

Data Platform

• Data Engineering Job Family

Intermediate and Senior Data Engineer Onboarding Timeline

Data Analyst

• Data Analyst Job Family

Intermediate and Senior Data Analyst Onboarding Timeline

Data Science

• Data Science Job Family

Intermediate and Senior Data Scientist Onboarding Timeline

Analytics Engineering

Analytics Engineering Job Family

Intermediate and Senior Analytics Engineer Onboarding Timeline

Data Management

• Data Management Job Family

Data Manager Onboarding Timeline