Outline your methodology for troubleshooting a silent failure in a complex, multi-stage data pipeline.

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Anonymous · Accepted Answer

I’m a POC for reviewing and building privacy compliant metrics on my team. I’ve
written a detailed guide on differential privacy and building privacy compliant
metrics.

We had the chance to build a rather granular metric in a new domain, about 20%
of the way through I realized our vanilla design did not work here because the
level of grain meant about 70% of the signal was suppressed from k-anon
filtering

We went back to the drawing board, I had an idea - instead of adding privacy
noise to raw conversions and using them in the metric (which was standard) I
came up with a design where we would compute the metric end-end within the
privacy sandbox and then add noise to the final aggregated metric. This overcame
the k-anon signal loss and made the noise added manageable, but at the cost of
30% more metric and pipeline build time.

I built a quick POC to validate the design and derisk and authored a spec doc
with the metric and privacy calculation. I collaborated with privacy and legal
teams to recognize this new design

Due to the increased build time, we only managed to hit a limited release (for
subset of surfaces that needed urgent unblocking) by the original launch date,
however we followed up a month later with a full release that was a success.

I realized the flaws in my own guide to building privacy metrics and updated the
framework to surface such issues as early as possible in design phase. The tech
spec on within box metric design was also used pretty widely across teams to
unlock a new set of privacy compliant metrics

Outline your methodology for troubleshooting a silent failure in a complex, multi-stage data pipeline.

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