This presentation exposes the Liv Up data platform, its technologies and organization design.

1. Data platform
sli.do: dataproducts

2. Achieving
autonomy in
the business
analytics team
Aligning people
and technology

3. The challenge
Product Manager Business Analyst Data Engineer Software Engineer

4. Handover is expensive and
inefficient
Hard and inefficient
Slow and expensive
Requires synchronization
Incentives must be aligned

5. What is Liv Up?

6. Goals
Maximize data value using
domain knowledge
Enable fast decision making
based on data
Reduce communications costs
Build a scalable data
architecture

7. Principles
Business context immersion and domain
knowledge building
Data is a first-class citizen in product
development and process design
Autonomy to build data pipelines
Responsibility for data quality

8. Business context
Squads
OKRs
Domain knowledge

9. Data in product and processes
Business analysts in groomings and
plannings
Business analysts are responsible for the
squad outcome
Capturing data is not always cheap

10. Autonomy and responsibility
Transparency: all analysts know where the data
come from
Standards: we have guidelines for data
transformation and modeling
Peer-review: pull requests and code review
guarantee consistency and knowledge dissemination
Testing: transformations are tested, preventing
outages and wrong data
Simplicity: minimize number of tools

11. Platform
demo
That’s how we do it :)

12. Raw data Master data
Transformation
Layer
Data Modeling
Data Consumption
Layer
Ingestion Layer
BigQuery
Federated Data
Source
BigQuery
Data Transfer
Service

13. Raw
Layer
Preparation
Layer
Master
Layer
Derived
Layer

14. Datasets in BigQuery

15. Dataform transformations
Dependency management
Job scheduling
Code versioning
Tests

16. Code review checklist

17. Testing data with assertions

18. Single and unified master dataset

19. Data Usage

20. Pros
- BAs have context, are independent, faster
and more effective
- Data engineers focus on platform instead
of ETL
- Engineering doesn’t bottleneck data
analysis
- Transparency. Everyone knows where the
data come from and how.
- People have access to all data and
transformations, avoid duplicate tables
Cons
- BAs are alone in the squads, require
chapter processes for knowledge
sharing
- BAs focused on squad context, could
miss big picture
- BAs code a lot. Requires training and
processes for scalability
- Works better with at least 1 BA per
squad
- Cloud costs are hard to track
Conclusion

21. We are hiring!
https://jobs.kenoby.com/livup
luiz@livup.com.br
Data Engineers
Data Product Managers
Data Scientists
Business Analysts
Software Engineers
Experimente Liv Up
Cupom: MEETUPDATA