Celonis
Celonis Engineering Team
We’re giving a brain to processes and could use yours to do it. Whether you’re focused on frontend, backend, or somewhere in between - we have challenges that need solving across our entire technology stack, from multi-cloud to database systems to real-time data streaming to our end-user experience.
Team FAQs
What's it like to work in Engineering at Celonis?
What's it like to work in Engineering at Celonis?
Engineers at Celonis build the Process Intelligence platform — the technology that helps the world's largest companies X-ray how they operate, save billions, and run more sustainably — while working at the frontier of the AI enterprise. As SVP Engineering Jorge Ferrer Zarzuela puts it, teams are building "new AI capabilities that actually take AI for real to the enterprise," giving large language models the process context they lack elsewhere. The scale is real: the platform handles over 95 terabytes of incoming data a month, 285 million requests a day, and 150+ Kubernetes clusters, so ownership and impact are felt everywhere.
What's the culture like in Engineering at Celonis?
What's the culture like in Engineering at Celonis?
Engineering culture at Celonis is defined by four things: high trust, high expectations, high scale, and high impact. It keeps a startup mentality at enterprise scale — engineers experiment, take ownership under the "We Own It" value, and can message a VP or the CTO and expect a reply, which isn't the norm at most companies. It's also genuinely collaborative and people-first across global hubs in Munich, Madrid, New York, and Bangalore, where "founding engineers" support each other in building teams from the ground up. As Jorge Ferrer Zarzuela says, Celonis suits people who want real challenges, big impact, and a strong team culture with talented colleagues worldwide.
What's the career growth like in Engineering at Celonis?
What's the career growth like in Engineering at Celonis?
Career growth in Engineering at Celonis comes from ownership and scale: engineers take on complex, real-world problems early, broaden their skills across massive systems, and shape the platform and its AI capabilities directly. Managers actively invest in people — Quality Engineering Manager Guillermo Cano, for example, grew his Madrid team and saw three of his engineers promoted to senior quality engineers. Fast-growing hubs like Bangalore, led by Senior Director Sarang Sahani, offer rare "founding engineer" opportunities to drive design, architecture, and delivery and set the standard for a new center of excellence.
Celonis Employee Perspectives
What attracted me the most What attracted me the most to Celonis is that it’s a company with huge ambition and huge potential for impact.”
At Celonis, we’re on a mission to make processes work for people, companies, and the planet. As our products evolve, so does our engineering footprint, and the new Bangalore Engineering Hub marks a major leap forward. This isn’t just an extension of our global engineering network; it’s a new center of excellence shaping the future of Celonis technology, powered by some of the brightest minds in software engineering.
The Bangalore Hub brings together startup energy and global scale, a fast-growing community of engineers building the next generation of Process Intelligence technology.
Employee Reviews
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What People Are Saying About Celonis
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Team Support: Colleagues are often seen as helpful, open, and willing to share knowledge across global hubs, creating a collaborative engineering environment. Blameless post-incident learning and approachable leaders further support day-to-day problem solving.
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Autonomy: Engineers are frequently encouraged to take end-to-end ownership, with a bias for action that moves ideas from design docs to production quickly. This “We Own It” norm lets teams shape systems and development practices, not just ship tasks.
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Innovation & Products: Work is commonly described as tackling modern data and platform challenges across AI/ML, automation, analytics, and distributed systems. The process-intelligence domain and PQL provide distinctive, technically demanding problems tied to real customer outcomes.
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At a glance
problems we solve
the scale of our product
