Bigeye combines automatic instrumentation, robust anomaly detection, and extensive customization to provide always-on insight into the quality of every table in every data pipeline. Bigeye can be deployed in as little as 15 minutes and enables teams to continuously monitor all aspects of their data quality, proactively detect and resolve issues, and ensure that every user — whether internal or external — can trust the data.
Why join us?
The Bigeye team consists of individuals with deep data experience who are collectively responsible for solving some of the most complex problems facing data teams today at gigabyte and petabyte scale.
Tripled our revenue and 5x-ed our customers in the last 6 months
Founded by early Uber data people
Funded by Sequoia Capital and Costanoa Ventures
Engineering at Bigeye
We work in cross-discipline, project focused teams. Most teams consist of a mix of Product, Design, Backend Eng, Frontend Eng, and Data science. We have one synchronous standup weekly and daily slack-based standups. Each project team has a lead which is responsible for their own sprint planning, although most common is a weekly planning meeting. We are highly collaborative and spend time on Product and Eng proposals during the project planning phase
One interesting technical challenge is scalability. Our customer base and usage are growing quickly, so any new features/services have to be able to handle the amount of traffic we will have in the future.
Another interesting challenge is building useable products and features on top of highly technical concepts. Bigeye monitors data quality using metrics, which are very powerful but potentially complex to understand on their own. We spend a lot of time collaborating on Product and Design in order to make these concepts consumable in a useful way for our customers.
Autometrics: When users connect Bigeye to their warehouse, it can be time consuming for them to set up dozens or hundreds of metrics manually. in order to alleviate this pain point, we implemented Autometrics. In this project, we profile the customer data and suggest the correct metrics on each table/column based on the results of profiling
ML Platform: At Bigeye, ML is at the core of what we do. We train thousands of ML models per day, and in order to support this requirement we had to build a ML platform that was robust and scalable to meet the needs of our customers
SLAs: Metrics on their own are powerful, but metrics grouped together are even more powerful. Let's say that you have some dashboard that you show weekly and want to monitor the underlying metrics on the data in that dashboard to alert you if there are any problems. With SLAs, you can group these metrics together into a single entity and set up alerting rules based on that grouped entity rather than the individual metrics.
Working at Bigeye
The Bigeye team consists of individuals with diverse experience who are passionate about solving the most complex problems facing data teams today.
Bigeye believes the strongest teams are built from differing but complementary perspectives. We've partnered with True Diversity to double down on our DEI&B goals to help us create a more inclusive workplace.
As part of our commitment to an inclusive workplace, we are happy to offer prospective engineers the chance to connect with our engineering employees who come from underrepresented backgrounds. It’s a way to get a better sense of our team and what it might be like to work with us.
If you’re interested in connecting with our team, be sure to bring this up during one of our introductory calls!
20 Days PTO
We provide Medical/Dental/Vision
Work from Home
We're a remote-first culture
Prioritizes diversity in hiring
Dedicated Human Resources team
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