Data and AI Engineer

• Partner with Services, Sales Operations, Marketing, Business Operations and other stakeholders to translate ambiguous business questions into well-defined analytical problems, challenge assumptions where needed, and communicate clear conclusions.
• Find, acquire, explore, integrate and prepare data from enterprise systems such as Salesforce, SAP and ServiceNow, ensuring appropriate data quality, lineage, security and governance.
• Analyze commercial, financial and operational data to explain performance and support recurring business cycles, including revenue and order-intake bridges, pipeline, backlog, installed base and variance analysis.
• Build and maintain curated datasets, Python/SQL data solutions, and semantic and metric layers on the Databricks Lakehouse and cloud platforms, with Azure preferred and AWS experience valued.
• Rapidly prototype and productionize agentic AI solutions that put trusted insights into business users' hands. Design agent tools and skills, curate semantic metadata, create evaluation suites, and use modern AI development harnesses and coding agents while maintaining sound Git, testing and CI/CD practices.
• Coordinate cross-functional data, analytics and AI initiatives from requirements through deployment, testing, validation and adoption, working effectively across business and technical teams.
• Proactively contribute to data and AI strategy and roadmap definition, evaluate emerging technologies, and share reusable methods and best practices across the team.

Doing meaningful work should come with fair, transparent rewards. The base salary range for this role is €78.187 –€130.312. We determine pay within the range using objective factors, like the skills the role requires, your relevant experience and the responsibility you'll have in this role, alongside internal equity and local market considerations.
We’ll share the full approach with you during the interview process, so you can make a clear, informed decision.
 

#LI-EU

#Healthcareinformatics

Enterprise Informatics is Philips' software business. Decisions about where to invest, which opportunities and accounts to prioritize, and how the business will perform depend on data from CRM, ERP, service, contracts and other enterprise systems. The Data and AI Engineer works at the intersection of business analysis, data analysis, data engineering and agentic AI. The role turns ambiguous business needs into well-defined analytical problems and scalable solutions, from data discovery and rapid prototyping through deployment and adoption. It is a hands-on technical role requiring direct work in Python and SQL, alongside the ability to explain methods, data quality and business implications to senior stakeholders.

• Bachelor's or Master's degree in Computer Science, Information Management, Data Science, Econometrics, Artificial Intelligence, Applied Mathematics, Statistics or an equivalent field. Minimum 5 years of relevant experience with a Bachelor's degree, or 3 years with a Master's degree, in data handling, analytics, data engineering, AI solution development or equivalent.
• Strong hands-on Python and SQL skills, including experience with Spark or PySpark, Delta Lake and practical software development practices.
• Practical Databricks experience spanning notebooks, Lakehouse/Delta, jobs or workflows, applications or agents, and deployment pipelines.
• Demonstrated hands-on experience building agentic AI solutions beyond a proof of concept, including evaluation, testing and production-readiness considerations.
• Strong data analysis capability, including statistical methods, data processing, reconciliation, and the ability to explain why data sources or metrics disagree.
• Experience with cloud platforms, preferably Azure; AWS experience is also relevant.
• Ability to coordinate cross-functional work, gather requirements, and communicate clearly in English with business leaders and technical stakeholders.
• Practical analytics-engineering experience with commercial and financial data, including revenue, subscription or licence models, pipeline, backlog and installed base, and related enterprise systems such as Salesforce, SAP and ServiceNow, ideally within a B2B software or technology business
• Modern agent frameworks and protocols, LLM evaluation practices, semantic modelling, dbt or Databricks metric views, and Power BI.
• Healthcare, medtech or another regulated industry is preferred