Coca-Cola Chile Distribution Data Flow
Data engineering contributor on a large-scale logistics and distribution data pipeline for Coca-Cola Chile, with direct client interaction and quality-gated delivery.
- AWS
- Python
- SQL
- PySpark
- GitHub Actions
- SonarQube
Problem
Coca-Cola Chile’s distribution and logistics operation generated data across production, transport, stock, losses, and damage — at operational scale. The data needed to flow reliably into analytical systems, with strong quality guarantees before any output reached client-facing dashboards.
My Role
Technical contributor with direct client interaction. I handled ETL development, data modeling, traceability work, requirement refinement, and issue resolution. I also participated in quality-gate workflows — coverage checks, Sonar analysis, and benchmark comparisons before delivery.
What I Built
- ETL flows processing distribution and logistics data across multiple operational dimensions.
- Data modeling for stock, transport, and loss tracking.
- Traceability mechanisms linking outputs back to source data.
- Quality gates: unit test coverage, SonarQube analysis, and benchmark validation runs before each delivery.
- Client communication: translating ambiguous business requirements into implementation specs and reporting back on tradeoffs.
Outcome
A delivery model that combined technical rigor (quality gates, coverage, Sonar) with stakeholder transparency. Working directly with the client required handling ambiguity in real time — converting verbal requirements into actionable specs on short cycles.