Data & AI Engineering Specialist

• Execute technical implementation of Sanofi's AI-Ready Data framework, ensuring data assets meet quality, trust, security, and accessibility standards for AI consumption

• Design and build end-to-end data pipelines supporting both structured and unstructured data for GenAI and next-generation AI applications

• Develop and maintain automated data preparation workflows using AI agent tooling, to enable enterprise-wide AI adoption

• Implement and maintain data governance processes, including metadata management and data cataloguing

• Maintain and curate metadata assets including data lineage, dictionaries, and catalogs; validate AI-generated metadata suggestions and manage metadata-as-code artifacts

• Implement compliance-as-code policies and automated access governance to ensure data security and compliance with GDPR, HIPAA, and other regulatory requirements; audit AI agent data access patterns

• Maintain documentation-as-code for data processes, models, and flows; validate auto-generated documentation to ensure audit-ready records in regulated environments

• Support stewardship workflows within governance platforms and collaborate with business stakeholders on data ownership

• Leverage automated observability tools and AI agents to monitor data catalog health; triage alerts and drive remediation across business units

• Collaborate with cross-functional teams globally to integrate data solutions with AI and data marketplace platforms

• Apply data quality engineering practices to identify, remediate, and prevent data quality issues in production environments

• Contribute to technical documentation, standards, and best practices for data engineering within the team

• Participate in code reviews, technical design discussions, and continuous improvement initiatives

Must Have:

• Bachelor's degree in Computer Science, Data Engineering, or a related technical field (Master's preferred)

• 5+ years of hands-on experience in data engineering, AI/ML data engineering, or a closely related technical role

• Experience working in pharmaceutical, life sciences, or other regulated industry environments preferred

• Demonstrated experience delivering data solutions at scale

• Familiarity with data protection regulations (GDPR, HIPAA) and compliance best practices

• Proficiency in data modeling, metadata management, data cataloging and data lineage practises; experience with schema-as-code and automated catalog management

• Proficiency in cloud data platforms, including one or more of the following: Snowflake, AWS, Microsoft Azure, Google Cloud Platform (GCP)

• Strong experience with data pipeline development using modern data engineering tools and frameworks (e.g., Apache Spark, dbt, Airflow, or equivalent)

• Proven experience in data management, with a focus on Informatica CDGC or similar data governance and compliance tools

• Proficiency in Python and/or SQL for data engineering and transformation tasks

• Hands-on experience in API/programmatic governance workflows

• Familiarity with data observability and monitoring tools and practices

• Understanding of data security, access control, and compliance requirements in regulated environments

• Strong analytical and problem-solving skills with attention to detail

• Effective communication skills, with the ability to explain technical concepts to non-technical stakeholders

• Ability to work collaboratively in global, cross-functional, and multicultural teams

• Continuous learning mindset with a passion for emerging data and AI technologies

Nice to Have

• Knowledge of pharmaceutical R&D, clinical trials, or manufacturing data domains

• Experience with Generative AI, Large Language Models (LLMs), and agentic AI systems

• Experience with AI/ML data preparation, feature engineering, and supporting production AI systems

• Familiarity with unstructured data processing, Natural Language Processing (NLP), and document AI technologies

• Understanding of responsible AI principles and AI governance frameworks

• Experience with real-time streaming and event-driven architectures (e.g., Apache Kafka, AWS Kinesis)

• Certification in Informatica CDGC, Collibra, or similar data governance platforms

• Relevant cloud or platform certifications (AWS, Azure, GCP, Snowflake, or equivalent)

• Exposure to enterprise AI data enablement platforms

Why J