Senior Product Manager – Scientific Data Engineering
hace 5 días
About Xebia
With over 20 years of experience, our global network of passionate technologists and pioneering craftsmen deliver cutting-edge technology and game-changing consulting to companies on the brink of transformation. Since 2001, we have grown from a Java company into a full-service digital consulting company with 5,500+ professionals working on a worldwide ambition.
We are organized in complementary service lines – teams with a tremendous amount of knowledge and experience within a particular field, such as Agile, DevOps, Data and AI, Cloud, Software Technology, Functional Programming, Intelligent Automation, and Microsoft.
We help the world’s top 250+ companies and category leaders overcome digital challenges, embrace innovation, adopt new technology, and implement new business models. In addition to high-quality consulting, we also provide offshoring and nearshoring services.
For more details, please visit
Role overview:
We’re seeking a Senior Product Manager to lead scientific data engineering initiatives in life sciences. You’ll manage data infrastructure and platform products that support chemistry, biology, and omics research, enabling researchers to store, access, integrate, and analyze complex datasets at scale while ensuring data quality, provenance, and compliance .
Responsibilities:
- Document existing product knowledge, use cases, metrics, and technical specs
- Analyze current products, workflows, and customer needs; propose transformation strategies
- Develop and deliver comprehensive product requirements
- Serve as primary business contact; map stakeholders and drive engagement
- Partner with data engineering, bioinformatics, and AI/ML teams to modernize cloud-native, scalable platforms
Requirements:
- Strong business analysis, stakeholder management, and requirements documentation skills
- Agile product development experience; proficient in Jira/Confluence
- Knowledge of modern data engineering stacks, SQL/NoSQL databases, and scientific workflows
- Ability to communicate effectively across data engineers, researchers, and AI/ML teams
Preferred:
- Hands-on experience with cloud platforms (AWS, GCP, Azure), data pipelines, and integration tools (Spark, Airflow, dbt, Kafka)
- Familiarity with data governance, quality, metadata, and HPC/cloud migrations
- Experience in regulated or pharmaceutical environments
This role is ideal for someone who wants to bridge science and technology , driving scalable solutions that accelerate scientific discovery.
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