Airbyte Doubles Employee Count, Announces Additions to 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. In this challenging time for companies to find qualified candidates with the unemployment rate for tech occupations at 1.7% in January, the company has added executives to lead finance, business development, 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,” said Michel Tricot, co-founder and CEO, Airbyte. “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.”
With its growing community of 7,000 data practitioners and 300 contributors, Airbyte is redefining the standard of moving and consolidating data from different sources to data warehouses, data lakes, or databases in a process referred to as extract, load, and, when desired, transform (ELT). Over the past year and a half, more than 20,000 companies have used Airbyte to sync data from sources such as PostgreSQL, MySQL, Facebook Ads, Salesforce, Stripe, and connect to destinations that include Redshift, Snowflake, Databricks and BigQuery.
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. Second, data teams often have to do custom work around pre-built connectors to make them work within their unique data infrastructure.