Job Summary

Required experience: approximately 12-16 years of relevant engineering experience. The Data Engineer - Proficient will design and build scalable data pipelines, data models and analytics-ready data products for Bayer platforms. The role supports AI readiness, reporting, governance and data product enablement across squads.

Key Responsibilities

• Build and optimize batch / streaming data pipelines, transformations and data integration workflows.
• Develop data models, data quality checks, data validation routines and analytics-ready datasets.
• Work with cloud data platforms, lakehouse / warehouse technologies and BI / visualization tools.
• Support data lineage, metadata, governance, access controls and documentation.
• Collaborate with AI Engineers and Data Scientists to prepare AI-ready datasets.
• Participate in sprint ceremonies, design reviews, data quality reviews and production support activities.
• Use engineering practices such as Git, CI/CD, testing and automated validation for data pipelines.

Skill Requirements

• Strong hands-on experience in Python, SQL and data engineering practices.
• Experience with AWS / Azure, Snowflake, Databricks and modern data platform capabilities.
• Knowledge of ETL / ELT, data modelling, data quality, metadata and data governance.
• Experience with PowerBI / Tableau or equivalent BI / reporting platforms.
• Ability to implement automated data validation, monitoring and pipeline reliability practices.

Other Requirements

• Strong understanding of privacy, compliance and enterprise data governance.
• Ability to work with business, platform, analytics and AI teams.
• Good to have: Agentic AI experience, data validation agents, data product assistants, AI-assisted data pipeline generation, metadata agents or AI-enabled data governance workflows.
• Good to have: Experience in life sciences, pharma, crop science or regulated enterprise environments.