Airbyte Doubles the Employee Count and Announces Additional Members to Their Leadership Team

Airbyte, creator of the fastest-growing open source data integration platform, has more than doubled the number of employees this year, adding 42 people to bring the total to 72 – with plans to grow to 200 by year-end. Companies face a difficult time finding qualified candidates in these times. With tech occupation unemployment at 1.7% in January this year, the company has appointed executives to lead finance, business and data policy.

With its growth trajectory and more than $181 million funding raised in 2021, Airbyte announced the company’s first hiring sprint in March. With Airbyte’s growth and success, the company has brought on new hires Otto Yeung as head of finance, Chris Tatarowicz as head of business development, and Patsy Bailin as head of data policy.

“These additions to our leadership team are strategically important representing key areas in helping us grow our business, focus on our relationships with developers, and ensure we have strong governance and policies that protect our users’ data,” Michel Tricot, Airbyte’s co-founder, is also the CEO. “With the tough competition for talent, we are so lucky to have not only doubled our workforce this year alone, but to have a strong team in place that is focused on users to help us reach our goals this year.”

Airbyte’s growing community of data professionals and 300 contributors is changing the way data is moved and consolidated to data warehouses, data lakes or databases. This process is known as extract, load and transform (ELT). Airbyte has been used by more than 20,000 companies to sync data from PostgreSQL and MySQL, Facebook Ads and Stripe and connect to destinations such as Redshift, Snowflake and Databricks over the past year.

Airbyte’s open-source data integration solves two problems: First, companies always have to build and maintain data connectors on their own because most less popular “long tail” data connectors are not supported by closed-source ELT technologies. A second problem is that data teams have to create custom connectors to work within their own data infrastructure.

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