PCU Shift Lead MWS cPB89

优缺点分析

角色解读

• Progress to senior data engineering roles or AI/ML engineering positions within Sanofi's AI-first transformation.
• Expand into specialized areas like data governance architecture, AI platform engineering, or data science management.
• Opportunity to lead larger data platform initiatives and mentor junior engineers as the team grows.
• Design and build end-to-end data pipelines for structured and unstructured data to support GenAI and AI applications.
• Implement data governance processes including metadata management, data cataloging, and compliance-as-code policies.
• Leverage AI agents and observability tools to monitor data health and automate data preparation workflows.
• Collaborate with global cross-functional teams to integrate data solutions with AI and data marketplace platforms.
• Proficiency in cloud data platforms (Snowflake, AWS, Azure, GCP) and modern data engineering tools (Spark, dbt, Airflow).
• Strong experience in Python and SQL for data transformation and pipeline development.
• Deep understanding of data governance, metadata management, and compliance regulations (GDPR, HIPAA).
• Knowledge of AI/ML data preparation, feature engineering, and production AI systems.

申请策略

• Tailor your resume to mirror the language of the job description, especially around 'AI-Ready Data' and 'compliance-as-code'.
• In your cover letter, express genuine interest in the healthcare mission and how your data engineering skills can accelerate drug discovery.
• Emphasize hands-on experience with cloud data platforms (Snowflake, AWS, Azure, GCP) and modern data pipelines (Spark, dbt, Airflow).
• Highlight any experience with data governance tools like Informatica CDGC or Collibra, and compliance knowledge (GDPR, HIPAA).
• Showcase projects involving AI/ML data preparation, feature engineering, or production AI systems.
• Demonstrate ability to work in regulated industries (pharma, finance, etc.) and with cross-functional global teams.
• If lacking, gain practical experience with GenAI or LLM data pipelines through side projects or courses.
• Obtain certifications in Snowflake, AWS, or data governance platforms (e.g., Informatica CDGC).

面试指南

• For pipeline design questions, use a structured approach: start with requirements (data sources, latency, compliance), then choose appropriate technologies, outline data flow, and discuss testing and monitoring.
• For compliance and governance questions, emphasize your understanding of regulations, how you implemented controls (access governance, audit trails), and your experience with compliance-as-code.
• For behavioral questions, use the STAR method (Situation, Task, Action, Result) to describe specific projects and your role.
• How would you design a data pipeline for both structured and unstructured data to support a GenAI application in a regulated environment?
• Describe your experience with data governance tools like Informatica CDGC. How did you implement metadata management and data lineage?
• Can you walk us through a time you ensured data compliance (GDPR/HIPAA) in a data engineering project?
• How do you approach monitoring data quality in production pipelines? What tools have you used?
• Explain how you would automate data preparation workflows using AI agent tooling.