Data and AI Engineer

Vacaturebank; Amsterdam; On-site; EUR 78,000 - 130,000; Full time

Job summary

Philips is seeking a Data and AI Engineer to bridge business needs and analytical solutions. You will work hands-on with Python and SQL, build datasets and AI workflows on Databricks, and communicate insights to senior stakeholders.

The role involves data discovery, rapid prototyping, and deployment across Azure platforms, with focus on governance, data quality, and scalable architectures.

Qualifications

• Bachelor's or Master's in Computer Science, Information Management, Data Science or equivalent.
• Minimum 5 years of relevant experience with Bachelor’s, or 3 years with Master’s in data handling and AI solutions.
• Hands-on Python and SQL with Spark/PySpark, Delta Lake and software development practices.

Responsibilities

• Translate ambiguous business questions into well-defined analytical problems and scalable solutions.
• Find, acquire, and prep data from enterprise systems like Salesforce, SAP, ServiceNow with quality controls.
• Analyze commercial and operational data to explain performance and support business cycles.
• Prototype and productionize agentic AI solutions and design evaluation suites.
• Coordinate cross-functional data, analytics and AI initiatives from requirements to adoption.

Skills

Python

SQL

Spark

Databricks

Azure

AWS

Power BI

Delta Lake

CI/CD

Git

Data Modeling

Data Governance

Education

Bachelor's or Master's in CS/Info Mgmt/Data Science

Tools

Databricks

dbt

Git

CI/CD pipelines

Power BI

Job description

Data and AI Engineer

Job Description

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.

Your role:

• 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.

You’re a right fit if:

• 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

Compensation & benefits

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.

How we work together
We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.

About Philips
Are you ready to do the work of your life to help the lives of others? Learn more about our business, discover our rich and exciting history and learn more about our purpose.

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