
Principal Data Architect - Remote in Canada
- Richmond, BC
- $102,000-211,800 per year
- Permanent
- Full-time
- Design and implement scalable data architectures that support the ingestion, storage, processing, and retrieval of medical imaging, clinical, and operational data
- Align data solutions with business and technology goals while ensuring adherence to governance policies, data standards, and best practices
- Develop and maintain robust ETL/ELT pipelines for both structured and unstructured data, ensuring performance, security, compliance (e.g., HIPAA), and cost-efficiency
- Lead data modeling efforts across transactional, analytical, and operational systems
- Evaluate and integrate emerging technologies to enhance platform capabilities and support advanced analytics
- Engage with internal leadership and customers to guide data architecture strategy and collaborate with AI/ML teams to enable advanced modeling and decision support
- Bachelor’s degree in Computer Science, Information Systems, or a related field; or equivalent professional experience
- 8+ years of experience in data architecture, data engineering, or related roles
- 5+ years of experience designing and implementing data solutions in cloud environments (Azure, AWS, or GCP)
- Solid experience with relational and NoSQL databases (e.g., SQL Server, PostgreSQL, Spanner, Couchbase, Oracle, MongoDB, Redis)
- Solid experience with Data warehousing or Data Lake design(e.g. Google BigQuery, Amazon Redshift, Snowflake)
- Experience with Business Intelligence Tools (e.g. Google Looker, Amazon QuickSight, Spotfire)
- Proficiency in data modeling, data warehousing, and data lake architecture
- Experience with data integration and ETL/ELT workflows and tools such as Spark, Flink, and Beam
- Experience with establishing or managing data governance frameworks and policies
- Experience with medical imaging platforms or healthcare IT systems
- Familiarity with healthcare data standards (e.g., DICOM, HL7, FHIR) and regulatory requirements
- Proficiency in scripting and programming languages (e.g., Python, SQL, Scala)
- Experience supporting AI/ML workflows with large-scale data infrastructure
- Familiarity with DevOps and CI/CD practices in data engineering contexts