Informacje o stanowisku

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Senior Data Analyst 

As a Senior Data Analyst, you will be a part of Bayers Pharma Data & AI Team with varying assignments according to project needs. You may collaborate with (analytical) data product teams, where the emphasis is on requirement engineering and data modeling, contribute to data visualization teams focused on semantic modeling, or support internal platform teams responsible for observability dashboards and analytics. While some projects will involve defining key metrics and developing scalable visualizations to enable self-service analytics, other assignments may concentrate primarily on data modeling, transforming complex inputs into actionable structures and insights that empower business decisions.

• 5+ years in analytics/BI roles, with a track record of delivering business impact in commercial or lifecycle environments.
• Expert in data wrangling and analysis skills; comfort with ambiguous questions and iterative problem framing.
• Good proficiency in Python, SQL, visualization tools such as PowerBI, Tableau, or ThoughtSpot, and data platforms such as Snowflake, Databricks, AWS.
• Good, hands-on experience with data cataloging and metadata management solutions (e.g. Collibra) and building related dashboards or reports will be a plus.
• Good understanding of data observability concepts and tools for monitoring data product reliability. Optional experience in managing platform observability solutions such as AWS CloudWatch/ OpenTelemetry/ Grafana to interpret data platform and pipeline SLAs and incidents.
• Hands-on experience in data modeling, following best practices and incorporating innovative approaches such as metadata-driven modelling.
• Ability to design semantic models and curate taxonomies, supporting federated data architecture and product-centric ownership.
• Demonstrated success in partnering with internal teams to improve metadata usage, discoverability, and governance.
• Strong communication, data storytelling, and stakeholder management skills; ability to translate complex concepts for non-technical audiences and drive evidence-based decisions.
• Experience with versioning and CI solutions such as GitHub and GitHub Actions is a must.
• Solid grasp of pharmaceutical data compliance requirements and best practices is preferred.
• Solid understanding of privacy/compliance constraints in healthcare/pharma will be a plus.
• Familiarity with Agile/Scrum methodologies.
• Self-driven, organized approach to managing multiple priorities and consistently meeting deadlines.

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Senior Data Analyst 

As a Senior Data Analyst, you will be a part of Bayers Pharma Data & AI Team with varying assignments according to project needs. You may collaborate with (analytical) data product teams, where the emphasis is on requirement engineering and data modeling, contribute to data visualization teams focused on semantic modeling, or support internal platform teams responsible for observability dashboards and analytics. While some projects will involve defining key metrics and developing scalable visualizations to enable self-service analytics, other assignments may concentrate primarily on data modeling, transforming complex inputs into actionable structures and insights that empower business decisions.

,[Translate user requirements into analytical plans and socialize trustworthy KPIs/metrics with clear ownership and documentation., Build curated datasets and analytical models in collaboration with analytics and data engineering teams, ensuring reproducibility and transparency., Develop and iterate dashboards in PowerBI and ThoughtSpot with strong data storytelling, guided navigation, and performance best practices., Align semantic definitions with business glossaries and maintain certified, well-documented models and dashboards in partnership with data governance teams., Define and implement data quality checks and anomaly detection for data products; proactively address drift and source changes with engineering partners., Enable self-service analytics through thorough documentation, training, and reusable assets, while tracking adoption and user satisfaction. Activate metadata to inform product iterations., Collaborate with cross-functional teams—including data product, source and consumption teams—to gather and refine requirements., Lead the development of semantic and data models, curate business glossaries, and establish standards for consistent data interpretation and trust within product teams and domains. Requirements: Python, SQL, Power BI, Tableau, Snowflake, Databricks Tools: GitHub, Agile. Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, Lunch card, International projects, Free coffee, Canteen, Bike parking, Playroom, Free beverages, In-house trainings, In-house hack days, Modern office, Startup atmosphere.

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