Overview In this Senior Data Architect role, you own end-to-end data product architecture to convert unstructured sources into enterprise-grade data products. You work with domain experts, data scientists, and AI engineers to translate requirements into scalable pipelines and schemas. You will lead architecture decisions for Trinity data products, including provenance, schema evolution, and entity resolution, while hands-on building robust data pipelines. The role blends design with implementation, with optional AI-assisted full-stack development for internal analysis interfaces. This is a high-impact opportunity to shape data-driven commercial decisions in biopharma.
Compensation / Benefits- base salary range: $156,000 - $234,000 per year
- annual discretionary performance bonus
- remote work options (as indicated by location multiple sites)
Responsibilities- Translate business requirements into data product architecture with domain experts
- Own architecture for Trinity data products: tooling/stack, schemas, grain, keys, entity resolution, provenance, and schema evolution
- Design and build normalization pipelines for heterogeneous and loosely-structured text-based sources
- Build multi-stage pipelines combining deterministic parsing, LLM-assisted extraction, and human review
- Optionally develop AI-assisted internal interfaces for analysts to review and modify outputs
Key requirements- 8+ years in data engineering and data architecture, delivering end-to-end data systems
- Depth in data modeling, grain/keys/entity resolution/provenance/schema evolution
- Strong SQL and relational/data warehouse expertise with performance optimization
- Strong Python for production pipelines, including orchestration, versioning, testing, and observability
- Experience normalizing messy semi-structured and unstructured sources (XML, PDFs)
- Ability to increase productivity with AI coding assistants
- Excellent written and verbal communication to present to non-technical stakeholders
- Excellent communication
- Ability to present technical concepts to non-technical stakeholders
- Collaborative engagement with business teams
- PySpark familiarity
- Git, Jenkins, Code Pipeline (CI/CD)
- Unix/Linux environments and shell scripting
