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Running a Data-Analytics-Engineering-Platform-Science Team

This guide is meant to help you run a data engineering team. By data engineering team, I mean any team that has at least one data engineer on board. I know that’s vague, but that’s the nature of the game, whether you call it analytics, data science, or data platform.

Data engineering teams cover lots of different kinds of functions to very different degrees:

  • Data engineering at its core - building and running complete data pipelines
  • Analytics engineering - transforming data (often using SQL)
  • Platform engineering - running data orchestrators BI tools and providing data products to other teams
  • Analytics - providing insights, dashboards, and reports to decision makers.

This guide tries to cover most of these functions and, in addition, all metafunctions.

CONTRIBUTE: FEEL FREE TO CONTRIBUTE GREAT RESOURCES. You can contribute links to podcasts, videos, books, tools, and blog posts. Just make sure to find the right category. There are still a ton of gaps, so please help me update this guide!

Leadership

Comprehensive Getting Started with DE Resources

Organising Teams

Communities/ Running Publications

Customers

Data Platform Engineering

Analytics

Data Engineering

Analytics Engineering

Data Product Management

CONTRIBUTE THIS

  • Scaling data teams
  • leadership resources (focused on actually leading, e.g. people management)
  • more video content
  • more on data product management (as long as relevant!)
  • ...