Table of Contents
- Data transformation
- • Kiro Dreams of Data
- • dbt 101 (EU and US-friendly)
- • Ask the experts: Customer product journey
- • How to build a reputation on more than just dashboards
- • Ask the experts: Version controlling your metrics
- • How to complete a data modeling project at a $6B company
- • Kimball in the context of the modern data warehouse: what's worth keeping, and what's not
- • Seven use cases for dbt
- • Quickstart your analytics with Fivetran dbt packages
- • Building a marketing attribution model with dbt
- Data testing
- Implementations + deployment
- Documentation + metadata
- The modern data stack
- Data dream teams
- • Lessons in prioritization: How to balance the 'quick questions' against your team's long-term plan
- • Getting started with technical blogging
- • How to structure a data team
- • Run your data team as a product team
- • Hiring a diverse data team
- • How to start your analytics engineering team
- • Supercharging your data team
- • Balancing creativity and proficiency as a data team
- • Evaluating an offer in the data space
- • Taking off with dbt: JetBlue's dbt journey!
- • Data dream teams: TripActions
- • Data dream teams: Netlify
How to start your analytics engineering team
At many organizations, dbt and the competency of Analytics Engineering are introduced well after the establishment of an analytics team. It's easy to agree in principal with all the benefits and value added by this new tool and analytics practice, but getting there can be a challenge. As with most tool implementations or team restructuring, there is often a long, painful transition from whatever was being done previously to the new future.
In this presentation we'll learn from Andres' experience implementing analytics engineering practices in both a greenfield situation (La Colombe) and his current successes (and failures!) of implementing analytics engineering at an already established organization (goPuff).
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Last modified on: Apr 19, 2022