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Clario

Senior Enterprise Architect

Clario

. Own the enterprise data architecture blueprint, principles, and reference patterns across ingestion, storage, transformation, and serving .

Posted 10/22/2026full-timeRemote • CanadaSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in enterprise data architecture, including data modeling, ingestion, and transformation strategies. Proficient in SQL and Python, with a strong focus on data governance, security, and cloud architecture.

Highest-signal resume keywords
Data Architecture OwnershipSQL FluencyPython ProficiencyData Governance ExperienceCloud Architecture Design

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
Data ModelingData TransformationBatch IngestionStreaming IngestionOrchestrationData QualityMetadata ManagementCI/CD StandardsEvent StreamingData Security
Soft Skills
LeadershipCollaborationCommunicationProblem-SolvingStrategic Thinking
Tools & Technologies
AirflowKafkaPostgreSQLOracleSQL ServerMongoDBAWSMDM PlatformsData PipelinesBI Tools
Industry Keywords
Clinical ResearchPharmaBiotechMedtechGCP-Regulated EnvironmentCDISC StandardsODMSDTMADaMData Maturity

Tech Stack

Tools & technologies
AirflowAWSCloudGoogle Cloud PlatformKafkaMongoDBOraclePostgresPythonSQL

About the role

Key responsibilities & impact
  • Own the enterprise data architecture blueprint, principles, and reference patterns across ingestion, storage, transformation, and serving
  • Define reference architectures for batch, CDC, near-real-time streaming, semantic layers, and federated queries
  • Set data modeling direction across operational, analytical, and clinical-standard data
  • Evaluate and govern technology choices in line with company-wide technology strategy
  • Partner with product and engineering teams on data contracts and schemas
  • Design storage strategy across lakehouse, warehouse, operational, and archival tiers
  • Define data delivery for BI, embedded analytics, ML/AI workloads, regulatory submissions, and sponsor data exchange
  • Co-own data security controls with InfoSec, including encryption, tokenization, access management, and audit
  • Set the foundation for governance, MDM, data quality, metadata, and cataloging
  • Establish CI/CD, observability, data testing, incident management, and orchestration standards
  • Serve as the data authority on the Architecture Review Board
  • Assess data vendors and SaaS platforms for architectural fit, data residency, GxP suitability, and total cost of ownership
  • Produce architecture decision records, reference implementations, and training
  • Lead a data maturity program and report progress to R&D leadership
  • Partner closely with the Senior Director of Data & Analytics
  • Report to the Chief Enterprise Architect

Requirements

What you’ll need
  • 5+ years in data-intensive roles, including 5+ years in senior data architecture or principal/staff-level data engineering
  • Hands-on ownership of the full data engineering lifecycle, from generation through serving, in production
  • Track record of measurably improving an organization’s data maturity
  • Experience working within or alongside an Enterprise Architecture function or Architecture Review Board
  • Fluency in SQL and Python (or another data engineering language), with ability to review production pipeline code
  • Experience with batch and streaming ingestion, including CDC and event streaming (Kafka or equivalent)
  • Practical experience with orchestration (Airflow or equivalent), data modeling, and transformation frameworks
  • Working knowledge of relational and document databases, including PostgreSQL, Oracle, SQL Server, and MongoDB
  • Familiarity with MDM concepts and at least one MDM platform
  • Experience designing on AWS or a comparable cloud provider, with attention to cost, security, and operability
  • Nice to have: experience in clinical research, pharma, biotech, medtech, or another GCP-regulated environment
  • Nice to have: knowledge of CDISC standards (ODM, SDTM, ADaM) and clinical data domains
  • Nice to have: exposure to AI/ML platform architecture, including feature stores, vector stores, RAG pipelines, and MLOps