Senior Data Engineer - Remote in Canada

UnitedHealth Group•Vancouver, BC

1d•CA$82,700 - CA$171,900•Remote

About The Position

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. For more than 20 years, we’ve supported global healthcare systems from Ireland and the UK, growing into a dynamic team of 2,100+ talented individuals. As we continue our strong record of stability and growth, we’re expanding the Stratus Imaging Analytics team—building analytics solutions for our Enterprise Imaging portfolio with customers across Europe, Canada, and the United States. In this role, you’ll join our Canada-based capability (working remotely within Canada) while collaborating day-to-day with a team that is primarily based in Ireland. We’re looking for a Senior Data Engineer who is passionate about delivering real customer value while driving innovation and continuous improvement. If you’re motivated by using data and analytics to help improve healthcare, we’d love to hear from you.

Requirements

6+ years of data engineering experience building and supporting production data pipelines
3+ years of hands-on experience with Google Cloud Platform, especially BigQuery, Cloud Storage, Dataproc, Cloud Workflows, Secret Manager, and IAM
Solid Python development experience, including writing and maintaining data-processing code and testable utilities
Experience with PySpark or Spark-based batch processing in cloud environments
Experience designing and supporting batch ingestion pipelines with orchestration, retries, failure handling, rollback, and auditability
Experience working with structured and semi-structured file ingestion patterns, schema validation, and upstream/downstream data contract enforcement
Experience implementing automated testing and CI/CD for data platforms, including unit tests, integration tests, and workflow/runtime validation
Experience with observability and operations for data systems, including monitoring, logging, incident response, root-cause analysis, and production support
Experience collaborating with analysts, product teams, and platform/cloud engineers to deliver reliable data solutions
Solid understanding of secure data handling, including encryption, secrets management, access controls, and least-privilege design
Proven solid SQL skills, including complex query design, performance tuning, stored procedures, and data quality validation logic
Proven solid documentation and communication skills, with the ability to own technical runbooks and operational handoff materials

Nice To Haves

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, or equivalent practical experience
GCP certification or equivalent cloud architecture/data engineering certification
Experience in healthcare, medical imaging, or other regulated environments handling sensitive data
Experience with BigQuery-based analytical modeling, incremental load patterns, deduplication, correction/replay logic, and late-arriving data handling
Experience maintaining cloud-native orchestration in YAML or workflow-as-code patterns
Experience tuning Dataproc/Spark jobs for reliability, scalability, and cost efficiency
Experience building data quality frameworks, reconciliation checks, and daily validation/monitoring dashboards
Experience with GitHub Actions, repository-based validation suites, and release controls for data pipelines
Experience supporting backfills, controlled reruns, and production incident recovery for large batch pipelines
Experience mentoring engineers and driving engineering standards for testing, code quality, and operational readiness
Experience in Agentic Development
Knowledge of file-based ingestion controls such as naming standards, tenant validation, hash checks, and archival/audit requirements
Familiarity with radiology, clinical, or multi-tenant healthcare data domains

Responsibilities

Design, develop and maintain data pipelines, to ingest, transform, and store healthcare data from various enterprise data sources
Analyze raw data sources, data transformation, structural requirements for new software and applications
Identify, design and implement internal process improvements, such as Data encryption, Data de-identification to ensure data security, Role-based data access, Automating manual processes, Optimizing data delivery, Tuning and optimizing data processes for greater scalability, etc.
Design, build and implement automation processes for production release of data engineering, machine learning and business intelligence processes
Code, test, document and maintain high-quality and scalable Big Data solutions
Define security and backup strategy for data solutions
Build and implement batch and near real time data pipelines
Help to ensure successful deployment of newly implemented data solutions
Works with less structured, more complex issues
Serves as a technical leader and mentor to others