Lead / Principal Deployment Engineer

Responsibilities

Initiate and Connect

• Conduct business discovery workshops to translate customers’ strategic objectives into high-impact Celonis use cases.
• Collaborate with customer IT and business stakeholders to define project scope, data requirements, and success criteria.
• Ensure data connectivity and extraction pipelines are properly configured, working with specialist resources when required.

Build and Create Value

• Direct AI deployment agents (Apollo) to build data models, process analyses, KPIs, and dashboards.
• Write clear specifications and validate AI-generated outputs against business requirements.
• Build and configure Celonis solutions across the Process Intelligence Graph, data transformations, process analysis, operational solutions, and AI-powered insights.
• Identify process inefficiencies and root causes using Celonis technology in collaboration with customers.
• Validate outputs for accuracy, business relevance, and alignment with customer context.
• Lead value creation workshops and define measures to improve process inefficiencies.
• Define and build KPIs to measure customer success and quantify business impact.
• Design and build operational solutions that drive value, adoption, and process conformance.

Rollout, Adoption, and Handover

• Enable key customer users to use and extend solutions independently.
• Manage project outcomes against timelines, scope, and customer expectations.
• Train and demonstrate solutions to customer users, guiding them toward sustained value.
• Document solution design decisions and configurations for future reference and handoff.

Domain and Industry Expertise

• Develop and maintain deep expertise in process domains such as Supply Chain, Order to Cash, Procure to Pay, and IT Service Management, or in relevant industry verticals.
• Translate domain knowledge into scalable solution patterns, reference architectures, and best practices.
• Identify and standardize high-value use cases while collaborating with Product and Business Apps teams to shape the platform roadmap.

Role Requirements

• 7+ years of experience in management or IT consulting, data or solution consulting, or process improvement consulting within software vendors, consulting firms, or system integrators.
• Ability to build and configure solutions on the Celonis platform, including data modeling, process analysis, KPI definition, and automation.
• Proficiency in PQL, SQL, and Python.
• Comfort working with AI-assisted development tools such as Claude Code, Codex, or Cursor.
• Ability to write clear specifications, evaluate AI-generated outputs, and iterate effectively.
• Strong understanding or deep expertise in enterprise business processes and financial metrics.
• Knowledge of standard ERP systems such as SAP and Oracle and process areas including Inventory Management, Order to Cash, Procure to Pay, and Accounts Payable is a strong plus.
• Strong consulting, workshop facilitation, presentation, stakeholder management, and delivery skills.
• Ability to translate business requirements into technical solutions and quantify business impact.
• Willingness to continuously develop expertise through experimentation and comfort with ambiguity.
• Bachelor’s or Master’s degree in Technology Management, Business Informatics, Computer Science, Engineering, Business Administration, or a comparable field.
• Fluency in English and German.
• Willingness to travel to customer locations up to 50% according to customer requirements and project delivery phases.

Compensation and Benefits

• Restricted Stock Units (RSUs) and merit-based refresh grants for full-time employees.
• Inclusive parental leave policy, including 24 weeks of fully [truncated in source].
• Opportunity to work with the Process Intelligence Graph and AI-driven business operations.

AI-Augmented Delivery Model

• Direct and validate AI agents rather than manually writing every line of code or building every analysis.
• Translate ambiguous customer needs into precise specifications that AI agents can execute.
• Continuously experiment with evolving AI capabilities and share effective approaches.
• Balance accelerated execution with quality and customer-specific judgment.
• Ensure solutions are robust, user-friendly, maintainable, and scalable.