Systems | Development | Analytics | API | Testing

If you are thinking about applying for an Engineering role at Bitrise (which we highly encourage), you probably want to know what happens after you hit that "apply" button. This post walks you through every step. It helps you decide whether we're the right place for you, gives you clarity before the interviews, and sets you up for success even before you apply.

For years, the mandate for System Integrators (SIs) was clear: lead the "cloud-first" migration. The promise was lower costs, greater agility, and seamless innovation. But for many enterprise customers, that promise remains unfulfilled. Instead of agility, organizations have inherited a complex, fragmented data estate. Data is siloed across on-premises legacy systems and multiple public clouds, creating governance headaches and inflating infrastructure costs.

June's releases share one thread: less setup between what you already know and a working test. Production Insights can turn a single real user session into a test case without a full Journey Map first. Katalon AI Assistant now reads the docs and boards your team already works in, shows its reasoning as it goes, and reaches apps your CI never could. Test management got safer to run at scale, with version history, ALM sync, and encrypted secrets. Here's everything that shipped.

Most data stacks weren’t built for AI agents. Take the ODI Assessment to find out if yours is ready.

The AI-analytics category has one operating assumption: AI’s job is to do more of what humans currently do. Adoption is measured by delegation surface. Success is when AI takes the whole task. Ask the executives running AI daily what they refuse to delegate, and you get a different story. Actually, you get the same story, from operators who do not know each other, running different companies, across different functions.

Living in South Florida, I've spent a lot of my career talking to customers about disaster recovery through the lens of hurricanes. Those conversations are easy because everyone understands the threat. We can watch a storm develop for days. Weather stations track every shift in direction. Data centers activate contingency plans. Business continuity teams prepare for impact.

You swapped in a better model. You fine-tuned it. You threw more tokens at the problem. And still — your agents hallucinate, break under load, and deliver answers that were accurate about three hours ago. The model isn't the problem. Gartner recently flagged that up to 40% of enterprise agentic AI initiatives are at risk of failure. Executives see that number and immediately audit their LLM provider. Their infrastructure team. Their prompts.