Data Team Organization

Data Team Organization

The Data Team Organization model is guided by five 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.
5. The need to foster trust, compliance and value driven insights.

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

1. Analytics Engineering: Transform raw data into clean, structured, and usable formats for data decision-making. The Lead Analytics Engineer serves as a stable counterpart for business departments and functional analytics teams.
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 Governance and Data Quality Team: Help build robust data governance practices and establish data quality frameworks for data quality monitoring and data quality improvement.

Data Team Operating Model

The Enterprise Data Team collaborates internally via Key Results. Key Results are planned on a quarterly basis and various team members from the four pillars of the Team can be assigned to a Key Result. The Key Result has a DRI who is the Directly Responsible Individual for the business outcome of the Key Result and leading the Team to success. Each respective pillar on the Team has flexibility to establish their own pillar specific ceremonies as well as processes on how they triage and assign P1-Ops and P3-Other issues that come up.

It is optional for team members to attend the ceremonies of other pillars. Team members are encouraged to attend other pillar ceremonies where attendance adds value.

At times, a pillar in the Enterprise Data Team may require collaboration and support from another pillar on an extended basis, lasting multiple quarters, and requiring consistent and deep support across P1, P2, and P3 issues. In these cases, a team member can be assigned as a stable counterpart to the pillar for an extended period of time with an end date established for the commitment where that team member will provide dedicated support across P1, P2, and P3 issues for the pillar. Expectations and capacity will be agreed at the start of the assignment and could change throughout in collaboration and agreement.

Below are the expectations of the DRI assigned to the Key Result:

1. Ensure completion of the opportunity canvas and ask for help when needed.
2. Schedule a work breakdown session with the Key Result Team. This can be either asynchronous or synchronous depending on the Key Result.
3. Schedule recurring stand-ups and working sessions as needed with the Key Result Team. This can be either asynchronous or synchronous and at a frequency that makes sense for the Key Result.
4. Provide a monthly update in the Key Result Issue in the OKR Project. Include percent complete and health status of the Key Result.
5. Raise risks and dependencies for successful completion of the Key Result with the data management team.

Analytics Engineering - Team and Stable Counterpart Assignments

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:

• 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

  graph LR;
  subgraph Data Engineering Roles
    supe:jde(Junior Data Engineer)-->supe:de(Data Engineer);
    supe:de(Data Engineer)-->supe:sde(Senior Data Engineer);
    supe:sde(Senior Data Engineer)-->supe:fde(Staff Data Engineer);
    supe:fde(Staff Data Engineer)-->supe:pde(Principal Data Engineer);
  end

  click supe:jde "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-engineer/#junior-data-engineer";
  click supe:de "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-engineer/";
  click supe:sde "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-engineer/#senior-data-engineer";
  click supe:fde "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-engineer/#staff-data-engineer";
  click supe:pde "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-engineer/#prinicipal-data-engineer";

Intermediate and Senior Data Engineer Onboarding Timeline

Data Analyst

• Data Analyst Job Family

  graph LR;
  subgraph Data Analyst Roles
    supe:ida(Data Analyst Intern)-->supe:jda(Junior Data Analyst);
    supe:jda(Junior Data Analyst)-->supe:da(Data Analyst);
    supe:da(Data Analyst)-->supe:sda(Senior Data Analyst);
    supe:sda(Senior Data Analyst)-->supe:fda(Staff Data Analyst);
  end

  click supe:ida "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-analyst#data-analyst-intern";
  click supe:jda "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-analyst#junior-data-analyst";
  click supe:da "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-analyst#data-analyst";
  click supe:sda "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-analyst#senior-data-analyst";
  click supe:fda "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-analyst#staff-data-analyst";

Intermediate and Senior Data Analyst Onboarding Timeline

Data Science

• Data Science Job Family

  graph LR;
  subgraph Data Science Roles
    supe:ds(Data Scientist)-->supe:sds(Senior Data Scientist)-->supe:stds(Staff Data Scientist)-->supe:pds(Principal Data Scientist);
  end

  click supe:ds "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-science/#data-scientist-intermediate";
  click supe:sds "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-science/#senior-data-scientist";
  click supe:stds "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-science/#staff-data-scientist";
  click supe:pds "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-science/#principal-data-scientist";

Intermediate and Senior Data Scientist Onboarding Timeline

Analytics Engineering

• Analytics Engineering Job Family

  graph LR;
  subgraph Analytics Engineer Roles
    supe:aae(Associate Analytics Engineer)-->supe:ae(Analytics Engineer);
    supe:ae(Analytics Engineer)-->supe:sae(Senior Analytics Engineer);
    supe:sae(Senior Analytics Engineer)-->supe:fae(Staff Analytics Engineer);
    supe:fae(Staff Analytics Engineer)-->supe:pae(Principal Analytics Engineer);
  end

  click supe:ae "https://handbook.gitlab.com/job-families/marketing/enterprise-data/analytics-engineer/#associate-analytics-engineer";
  click supe:ae "https://handbook.gitlab.com/job-families/marketing/enterprise-data/analytics-engineer#analytics-engineer-intermediate";
  click supe:sae "https://handbook.gitlab.com/job-families/marketing/enterprise-data/analytics-engineer#senior-analytics-engineer";
  click supe:fae "https://handbook.gitlab.com/job-families/marketing/enterprise-data/analytics-engineer#staff-analytics-engineer";
  click supe:pae "https://handbook.gitlab.com/job-families/marketing/enterprise-data/analytics-engineer#principal-analytics-engineer";

Intermediate and Senior Analytics Engineer Onboarding Timeline

Data Governance and Data Quality

Data Governance and Quality Analyst Job Family

• Data Governance and Quality Analyst Job Family

  graph LR;
  subgraph Data Governance and Quality Analyst Roles
    supe:adgq(Associate Data Governance and Quality Analyst)-->supe:dgq(Senior Data Governance and Quality Analyst);
    supe:dgq(Data Governance and Quality Analyst)-->supe:sdgq(Senior Data Governance and Quality Analyst);
    supe:sdgq(Senior Data Governance and Quality Analyst)-->supe:sfdgq(Staff Data Governance and Quality Analyst);
  end

  click supe:adgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-analyst/#data-governance-and-quality-analyst-associate";
  click supe:dgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-analyst/#data-governance-and-quality-analyst-intermediate";
  click supe:sdgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-analyst/#senior-data-governance-and-quality-analyst";
  click supe:sfdgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-analyst/#staff-data-governance-and-quality-analyst";

Intermediate and Senior Data Governance and Quality Analyst Onboarding Timeline

Data Governance and Quality Program Manager Job Family

• Data Governance and Quality Analyst Job Family

  graph LR;
  subgraph Data Governance and Quality Program Manager Roles
    supe:dgqp(Data Governance and Quality Program Manager)-->supe:sdgqp(Senior Data Governance and Quality Program Manager);
    supe:sdgqp(Senior Data Governance and Quality Program Manager)-->supe:sfdgqp(Staff Data Governance and Quality Program Manager);
  end

  click supe:dgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-program-manager/#data-governance-and-quality-program-manager";
  click supe:sdgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-program-manager/#senior-data-governance-and-quality-program-manager";
  click supe:sfdgq "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-governance-and-quality-program-manager/#staff-data-governance-and-quality-program-manager";

Data Management

• Data Management Job Family

  graph LR;
  subgraph Data Management Roles
    supe:md(Manager, Data)-->supe:smd(Senior Manager, Data);
    supe:smd(Senior Manager, Data)-->supe:dd(Director, Data);
    supe:dd(Director, Data)-->supe:sdd(Senior Director, Data);
  end

  click supe:md "https://handbook.gitlab.com/job-families/marketing/enterprise-data/manager-data/#manager-data-intermediate";
  click supe:smd "https://handbook.gitlab.com/job-families/marketing/enterprise-data/manager-data/#manager-data-intermediate";
  click supe:dd "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-and-insights-executive/#director-data-and-insights";
  click supe:sdd "https://handbook.gitlab.com/job-families/marketing/enterprise-data/data-and-insights-executive/#senior-director-data-and-insights";

Data Manager Onboarding Timeline

Tool Technology Tandem

Tool Technology Tandems (TTT) are supporting to get the maximum value out of business opportunities we have in the Data Program. TTT are experts in a specific (software) tool or technology to support business opportunities or challenges we have by leveraging the tool or technology to the maximum. Although this is not the goal, we want to get the maximum value out of our technology stack. At the moment we see that we are not leveraging our technology stack to the maximum, where there are useful features or opportunities in our technology that could support in fulfilling business opportunities.

The reason is that from the technology side we don’t know the business and from the business side we don’t know the technology. The TTT will bridge this gap by understanding the needs and bring this together in a technological way. We expect from TTT to do consulting, guiding and educating.

Note: TTT will not search for business opportunities to use any tool feature. TTT has to understand business opportunities and translate this into what software could bring to the table.

A single TTT consists of minimum 2 and maximum 3 GitLab Team Members with different roles. There are no requirements in which team a Team Member is part of(so this could be outside of the central Data Team as well) as long as the TTT meets the expectations described below.

What do we expect from TTT

• We expect TTT to get in touch with our business partners and all functions that contribute to the data program or work with our Data Platform, to understand their challenges.
• We expect TTT to get up to date with the latest in their area. They understand the full capabilities of the tool / technology, have regular touchpoints with the respective vendor and have a good understanding of the latest released features.
• TTT will guide and educate our business partners.
• TTT will initiate design-spikes for quarterly planning.

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