Founded in 2020, Airbyte is the context layer for production-grade AI agents. AI agents fail in production for one reason: they can't see the business. They make scattered API calls at runtime, burn tokens reconciling fragmented data, and break under real workloads. Airbyte solves this by giving agents unified, permission-aware access to the operational data scattered across the tools companies actually run on (CRM, billing, support, product, internal systems) through a hybrid architecture that combines large-scale replication (for cross-system search and discovery) with real-time fetching (for fresh operational state). It's the combination production agents actually need, built on the connector footprint we've hardened over six years.
We've raised $181M from Benchmark, Accel, Altimeter, Coatue, Y Combinator, and others. Today, 25,000+ companies sync data with Airbyte's 600+ connectors. Open source remains core to how we build, because the data foundation under your AI agents is too important to be a black box.
Rating Reviews
Rating is calculated based on
3
reviews and is evolving.
Pros: The team here is incredibly smart and dedicated. It's refreshing to work on an open-source product with such a strong community following. Collaboration is excellent, and you're often working with cutting-edge technology in the data engineering field. The remote-first culture and flexibility are huge pluses for work-life balance.
Cons: As with many fast-growing startups, processes can be a bit fluid, and sometimes priorities shift quickly. Documentation could be more robust in certain areas, and scaling some internal systems is an ongoing effort. Leadership is generally accessible, but more structured communication about long-term strategy would be beneficial.
Advice to Management: Keep fostering the strong community and engineering-driven culture. More transparent communication about strategic shifts will help everyone align and navigate growth effectively.
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Pros: The best part is definitely the team and the open-source mission. Everyone I worked with was smart, driven, and genuinely collaborative. It's a fast-paced environment with a ton of learning opportunities, especially if you're interested in data engineering and API integrations. The remote-first culture makes work flexibility really shine.
Cons: Being a fast-growing startup, processes can sometimes feel a bit ad-hoc, and communication clarity could be improved during rapid scaling phases. Occasionally, project priorities can shift quickly, leading to a bit of context-switching. It would be great to see more formalized career pathing discussions.
Advice to Management: Continue to invest in clear communication channels and documentation as the company scales. More formal career development discussions could benefit engineers looking for long-term growth within the company.
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Pros: The opportunities to contribute to an open-source project like Airbyte are immense. As a remote employee, I appreciated the flexibility and trust given by leadership. The team collaboration, especially within the engineering department, is generally strong. There's a real drive to innovate in the data pipeline and ETL industry, which makes for exciting work.
Cons: The rapid growth means processes can sometimes feel a bit ad-hoc, leading to occasional confusion or duplicated efforts. Workload can be unpredictable at times, especially around major releases or feature sprints. While flexibility is high, the intensity can sometimes blur lines, requiring conscious effort to disconnect. Communication could be more streamlined.
Advice to Management: Continue to refine internal communication channels and processes to keep pace with rapid scaling. Clearer project roadmaps and workload forecasting would help manage engineer expectations and prevent burnout.
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How does salary negotiation typically work for data engineering roles at a growing startup like Airbyte?
During my interview process for a data engineer position at Airbyte, I found they were open to discussing salary based on my experience, especially considering the fast-paced tech industry. The compensation package was competitive for a Series B company in the SaaS space.
What's the dress code like at Airbyte for a software engineer role?
As a software engineer at Airbyte, I found the dress code to be very casual and relaxed, which is typical for tech companies in San Francisco. You'll see a lot of t-shirts and jeans, so comfort is key here.
What is the typical salary range for a Data Engineer at a fast-growing, remote-first company like Airbyte?
Based on what I've seen in the open source data engineering community and discussions around Airbyte's compensation, salaries for Data Engineers in the US, especially at rapidly scaling SaaS companies, can range from $120k to $180k annually, depending on experience and specific responsibilities.
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