San Francisco, CA
Problem Analytics are hard to get right – especially for complex business models (like API-first SaaS). Building a flexible analytics stack is typically a challenging, non-trivial project. Everyone wants accurate, comprehensive data that can be easily consumed by teams across the company, but getting there hasn’t always been easy. This often requires a team of data engineers to build, test, and monitor a custom ETL (extract, transform, load) pipeline that cleans, aggregates, and loads data from
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