Platform work is easy to do without evidence and hard to defend that way. This is the toolset I use to know whether a platform bet actually paid, and to argue for the next one with something better than conviction.
| Technology | Depth | What I did with it |
|---|---|---|
| SQL | Expert | Primary analysis tool across pricing, adoption, and platform metrics. |
| Python | Practiced | Analysis pipelines and internal tooling. |
| Snowflake | Practiced | Warehouse layer for platform and business reporting. |
| Tableau | Practiced | Executive-facing dashboards and adoption reporting. |
| Wavefront | Practiced | Infrastructure metrics and alerting. |
| Splunk | Practiced | Log analysis for incident and reliability work. |
| scikit-learn | Familiar | Segmentation and forecasting models. |
| Databricks | Familiar | Evaluated as a platform for analytics and ML workloads. |
Platform adoption metrics lie more than product metrics do, because usage is often mandatory. The number that matters is whether teams would still choose the paved road if you removed the pressure to use it.