Senior Enterprise Architect

Role overview Set the enterprise architecture for data platforms, from ingestion and storage through transformation and delivery to analytics, AI, and regulated consumers. This is a senior, hands-on individual contributor role that combines technical design authority with practical work in code, data, and architecture decisions.

Responsibilities - Own data architecture principles, blueprints, and reference patterns for batch, change-data capture, streaming, semantic layers, and federated queries. - Set direction for data models and storage across lakehouse, warehouse, operational, and archival systems. - Partner with product and engineering teams to design schemas and data contracts for downstream use. - Define how data is served to business intelligence, embedded product analytics, machine learning, regulatory submissions, and data exchange use cases. - Establish architecture for security, governance, master data, quality, metadata, cataloging, pipeline testing, observability, and orchestration. - Review major data architecture decisions, assess vendors and platforms, and provide decision records, reference implementations, and training.

Requirements - At least five years in data-intensive work, including senior data architecture or principal/staff-level data engineering. - Production experience across the data engineering lifecycle, from generation through serving, and a record of improving data maturity. - Experience with enterprise architecture functions or architecture review boards. - Fluency in SQL and Python or another data engineering language, with ability to review production pipeline code. - Practical knowledge of batch and streaming ingestion, CDC or event streaming, orchestration, data modeling, and transformation frameworks. - Experience designing on AWS or a comparable cloud, with attention to security, cost, and operational reliability.

Nice to have - Experience in clinical research, life sciences, medical technology, or another regulated environment; familiarity with clinical data standards and domains. - Exposure to AI/ML platform architecture, including feature stores, vector stores, retrieval-augmented generation, or MLOps.