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Kyle Kirwan

Chief Product Officer, Bigeye

Kyle Kirwan is Co-Founder and Chief Strategy Officer of Bigeye, where he leads strategic partnerships, prototype development, and other zero-to-one projects.

Kyle’s journey to founding Bigeye began at Uber, where he helped scale the company’s experimentation and data platforms during a period of hypergrowth. As a product leader and former founding data scientist on Uber’s experimentation platform, he worked on standardizing metrics across thousands of A/B tests that shaped rider, driver, and pricing experiences for millions of users.

It was at Uber that Kyle met Egor Gryaznov. Shortly after Egor joined, he launched Uber’s first SQL bootcamp. Kyle signed up partly out of curiosity, and partly to make sure the new guy actually knew his stuff. They quickly bonded over giving each other increasingly complex SQL challenges to solve.

As Uber’s data ecosystem grew to hundreds of petabytes and thousands of weekly users, Kyle saw a pattern emerge: testing the data pipelines was valuable but didn’t scale. His team experimented with using machine learning models on the daily data profiles of tables in the data lake to see if anomalies could be identified without manually writing data quality checks. This technique would later be termed data observability.

In 2019, Kyle and Egor co-founded Bigeye to use the lessons learned at Uber to transform data management in the enterprise. Today Bigeye serves some of the world’s largest organizations and ensures their data is trustworthy, and that their enterprise AI initiatives are grounded in that trusted data.

recent Posts from this author

Engineering

Data in Practice: Data reliability tips from a former Airbnb data engineer

We spoke with Dzmitry Kishylau, a former member of Airbnb’s Trust and Safety team, to learn how they approached data reliability and get from-the-trenches tips.

Kyle Kirwan
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Engineering

How to calculate the ROI for data observability

On the one hand, businesses are more data driven than ever before. On the other hand, data pipelines are increasingly complex and error prone. Is it time to invest in data observability?

Kyle Kirwan
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Company

5 ways data scientists and ML engineers advance their careers and help hit business targets

5 ways your team’s progress, and your career, are held back by bad data quality, and what you can do to address it.

Kyle Kirwan
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Engineering

How to become a Data Reliability Engineer

There's a a need for a new kind of role within the data organization – one dedicated to the quality, observability, and maintenance of data. Enter the data reliability engineer.

Kyle Kirwan
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Thought leadership

Data observability, for any data team’s structure

Data teams tend to fall into one of three shapes. That shape will That will dictate the best strategy for rolling out and managing observability over your data, pipelines, and assets like analytics dashboards and machine learning models.

Kyle Kirwan
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Engineering

The three types of observability your system needs

Most modern software systems include infrastructure, data, and machine learning models. All three need observability, but each has different requirements, workflows, and personas. Let’s take a closer look!

Kyle Kirwan
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