When it comes to building a successful data engineering team its important to understand and structure the team as if the data provided to the business were a product itself (a product in which only customer is the internal stakeholders of the company).
This team structure proposes to create three main roles in a data team, a backend engineering team that builds and maintains the data models used for the business.
A Frontend data team of analysts that build the reports and dashboards that will be consumed by the business.
And a Data infrastructure architect that maintains the data pipeline operational and at scale to ensure the data build and pipeline is operating at the optimal cost and rate.
Company goals and objectives are translated into work items and tasks through the data development funnel, by doing regular sprint deployments the data team can continuously deliver progress and maintain the business analytics processes running.
Important here to include the ability of members of the business to provide user stories based on the initiatives of the company and goals, this is where the rubber meets the road, the team builds what the stakeholders ask based on what the company aims to achieve.
Continuous collaboration across all departments in the business is critical. And implementing a system to track requests, questions, bugs and feedback in an organized, trackable and open view is crucial.
Ticketing systems like Jira, Zendesk etc are great for this purpose to ensure no requests are getting left behind and allow the ability for users / (internal) customers be kept updated of their questions and requests.
As well as organize the work that is allocated to each team member.
As data grows in a business its important to have a detailed and updated data governance model.
To implement a data governance model the business a Data governance council is the best course of action, this council is composed of different business unit heads and stakeholders that consume and use data on a regular basis.
Defining the data policies and data archival procedures ensures the business is always functioning with a clean data model at the latest results.
Data Stewards are key elements as they act as the representatives of their unique teams into the data council and ensure that the policies set up by the council are followed up on their respective systems of management.
