The Data Team Organization model is guided by three primary business needs:
Based on these needs, the Data Team is organized in the following way:
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.
Data Fusion Team Name | Data Champion Team | Data Champion Name | Manager, Data | Lead Analytics Engineer | Function Analyst(s) | Analytics Engineer(s) | Data Engineer(s) |
---|---|---|---|---|---|---|---|
Research and Development Data Fusion | Product Data Insights | @cbraza |
@mdrussell (Acting Manager) |
@mdrussell |
@cbraza @eneuberger @nicolegalang @dpeterson1 @nraisinghani @matthewpetersen |
@jeanpeguero @michellecooper |
@rbacovic |
Customer Success | @jdbeaumont |
@mdrussell (Acting Manager) |
@mdrussell |
@bbutterfield @marntz |
@mdrussell @michellecooper |
||
Go to Market Data Fusion | Sales Strategy and Analytics | @aileenlu |
@nmcavinue |
@snalamaru |
@nfiguera @mvilain |
@chrissharp |
@paul_armstrong @Rigerta |
Marketing Strategy and Analytics | @christinelee |
@jahye1 @rkohnke @degan |
|||||
Online Sales and Self-Service | @mfleisher |
@mfleisher |
|||||
Financial Analytics Data Fusion | Corporate Finance | @james.shen |
@nmcavinue |
@chrissharp |
@vagrawalg @dgupta5 @smishra27 |
@chrissharp |
@paul_armstrong |
GTM Finance | @alixtucker @nbernardo |
@ofalken @vagrawalg @dgupta5 @smishra27 @kkarthikeyan |
|||||
Engineering Analytics Data Fusion | Engineering Analytics | @cdeleon_gitlab |
@nmcavinue |
@pempey |
@lmai1 @ddeng1 @raulrrendon @clem.lr |
@pempey @lisvinueza |
@jjstark |
General and Administrative Data Fusion | People Analytics | @aperez349 |
@nmcavinue |
@pempey |
@aperez349 @mccormack514 |
@pempey @lisvinueza |
@Rigerta |
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.
In support of the Data Fusion Team, the Manager, Data fulfills the below responsibilities from the Senior Manager, Data Job Responsibilites:
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:
In support of the Data Fusion Team, the Lead Analytics Engineer fulfills the below responsibilities from the Senior Analytics Engineer Job Responsibilites:
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.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:
Critical to successful Data Fusion Teams are the following elements:
We encourage our stakeholders to follow along with our issue boards to understand the scope of work:
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:
By Day 30 | By Day 60 | By Day 90 | By Day 120 |
---|---|---|---|
Complete People and Data Onboarding | Perform triage activities | Extract new data sources | Own a specific area of the data platform |
Create a MR to contribute to handbook or templates | Investigate incidents and issues | Work on OKR assignments | Propose new ideas and come up with Data Platform improvement initiatives |
Understand the current setup of the data platform | Make small/corrective changes to the platform infrastructure or data pipelines | Contribute on work breakdown |
By Day 30 | By Day 60 | By Day 90 | By Day 120 |
---|---|---|---|
Complete People and Data Onboarding | Extend an existing Sisense dashboard or complete the triage phase for a dbt issue | Run a project end-to-end as DRI with support from a Data Fusion Team | Create ERDs/Data Artifacts (e.g. dashboards) or complete a product evaluation |
Start attending Data Fusion Team and Business Team synchronous meetings | Perform triage activities | ||
Complete First Issue: S to M T-Shirt Size |
By Day 30 | By Day 60 | By Day 90 | By Day 120 |
---|---|---|---|
Complete People and Data Onboarding | Meet stakeholders across the organization | Re-train or enhance an existing data science model | Make a contribution to improve the Data Science handbook, packages, or processes |
Start attending Data Science Team meetings | Refine/improve one data science dashboard | Work on OKR assignments | Take ownership of at least one quarterly OKR |
Understand the current data science systems and processes |
By Day 30 | By Day 60 | By Day 90 | By Day 120 |
---|---|---|---|
Complete People and Data Onboarding | Extend an existing dbt Trusted Data Models | Run a project end-to-end as DRI with support from a Data Fusion Team | Create ERDs/Data Artifacts |
Start attending Data Fusion Team and Business Team synchronous meetings | Perform triage activities | ||
Complete First Issue: S to M T-Shirt Size |
By Day 30 | By Day 60 | By Day 90 | By Day 120 |
---|---|---|---|
Complete People, Data, and Manager Onboarding | Meet everyone on the team and business data champions | Complete a Team Assessment | Draft a people development Roadmap |
Understand the current setup of the data platform | Work on OKR assignments and map them to the data platform | Lead discussions with Users/Stakeholders on initiatives and OKRs | Draft a program development Roadmap (Process Improvements /Future State) |
Add a new page to the handbook | Make regular contributions to the handbook spanning your area of management | Become DRI for major portions of the Data Handbook | System/Application Change Control Management of one or more modules |