Senior Data Integration & AI-Ready Engineer

Pfizer, S.A. de C.V

Germany (OH)

On-site

USD 92,000 - 121,000

Full time

14 days+

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Benefits offered by this job

BRAVO Award program

Pfizer in Balance

Retirement benefits

On-site work in Freiburg

Job summary

Pfizer Deutschland is seeking a Senior Engineer to drive data integration and system orchestration across site Digital and Automation environments in Freiburg. You will coordinate execution of site data strategy, enforce data standards, and ensure compliant, validated data flows for analytics and AI initiatives.

The role requires a technical degree and 2–4 years in IT/infrastructure or process automation, plus 1–2 years in a regulated pharma setting.

Qualifications

Bachelor’s degree in engineering, Computer Science, Information Systems, Life Sciences, or related technical field.
2-4 years’ experience in information technology systems/infrastructure or specialist knowledge of process automation/process control systems.
1-2 years in a regulated manufacturing environment (pharma or similar preferred).
Experience with Lean/Operational Excellence and project/program management (PMI, Agile, SDLC).
IT infrastructure management experience (ITIL) and GMP regulatory requirements experience.

Responsibilities

Coordinate and contribute to the development, documentation, and execution of the site end-to-end data strategy in alignment with network direction.
Apply and reinforce data standards, ensuring consistency, integrity, and compliance in data structures and usage.
Ensure data-related work complies with quality, validation, cybersecurity, and regulatory requirements per SOPs and governance.
Design, implement, and support core system integrations and data flows across system layers.
Monitor data connectivity, identify issues, and resolve data quality gaps or performance risks.
Develop and manage site historians and data platforms to enable reporting and analytics.
Maintain asset, configuration, and data mapping documentation for validation and inspection readiness.
Support scaling and industrialisation of AI and analytics use cases.
Collaborate with automation, IT, quality, and business partners to deliver shared outcomes.