Senior Enterprise Architect

clarioclinical; United States; Remote; USD 180,000 - 220,000; Full time

Job summary

clarioclinical is seeking a senior data architect to set the enterprise data platform architecture across ingestion, storage, transformation, and delivery to analytics and regulated consumers.

You will lead by example in code, data, and architectural decisions, partnering with product and engineering to define schemas, contracts, and secure, observable data pipelines. This is a hands-on IC role with strategic impact.

Qualifications

• 5+ years in data-intensive work as senior data architect or staff data engineer.
• Production experience across the data engineering lifecycle from generation to serving.
• Experience with enterprise architecture functions or architecture review boards.
• Fluency in SQL and Python or similar data engineering language.
• Experience with batch and streaming ingestion, CDC/event streaming, orchestration, data modeling, and transformations.
• Experience designing on AWS or other cloud with focus on security and reliability.

Responsibilities

• Own data architecture principles, blueprints, and reference patterns for batch, CDC, 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.

Skills

SQL

Python

Data modeling

AWS

Data governance

Enterprise architecture

Job description

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.