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Salary: USD 165,000 - 206,000 / annual
Pivot Bio is pioneering advances in agriculture by harnessing naturally occurring microbes to provide nutrients to crops, helping farmers improve yields while reducing environmental impact.
You will define and steward the enterprise information backbone connecting Product Innovation, Agronomy, Manufacturing, Commercial, and Digital products. This role is centered on information architecture: designing durable semantic models, traceability frameworks, and evidence systems that support product performance analysis, scientific discovery, operational execution, and AI-enabled decision making.
Key responsibilities include:
- Own enterprise information architecture strategy and standards across R&D, Agronomy, Manufacturing, Commercial, and Digital domains
- Design canonical models, master data relationships, and cross-domain integration patterns
- Define semantic standards for observations, results, recommendations, outcomes, and evidence
- Lead logical and physical data modeling efforts within Databricks Lakehouse, designing Silver and Gold layers
- Establish metadata, lineage, versioning, and traceability frameworks
- Partner with Product, Engineering, Data Science, Manufacturing, and Commercial teams to translate business requirements into durable data models
- Review architecture designs and validate implementations through hands-on modeling, SQL analysis, and proof-of-concept work
- Drive data governance practices focused on quality, stewardship, reproducibility, and trust
- Provide a phased architecture roadmap for modernization and migration of existing data assets
This is not primarily a cloud infrastructure architect, integration engineer, or BI/reporting architect position. Success requires deep information architecture capability, including semantic modeling, domain design, lineage, traceability, and enterprise data stewardship.
REQUIREMENTS:
- 10+ years in Data Architecture, Information Architecture, or Enterprise Data Modeling
- Deep expertise in conceptual, logical, and physical data modeling
- Experience with canonical modeling, Data Vault, metadata management, MDM, and lineage
- Hands-on experience with Databricks, Snowflake, or modern Lakehouse platforms
- Strong SQL skills and ability to validate models directly against data
- Experience in regulated, scientific, manufacturing, healthcare, life sciences, or evidence-sensitive environments preferred
- Demonstrated ability to balance governance rigor with pragmatic execution
- Must be authorized to work in the United States
