Data Engineer

Role & responsibilities

• Developing and maintaining Pyspark and Ibis-based transform functions to convert healthcare data from TELUSs suite of EMRs into standardized FHIR R4 resources (Patient, Encounter, Observation, MedicationRequest, etc.)
• Optimizing Apache Flink streaming jobs for high-volume healthcare data processing with low latency requirements
• Collaborating with healthcare domain experts to ensure FHIR compliance and clinical data accuracy
• Working with Kafka streaming infrastructure to handle real-time data ingestion and distribution
• Implementing comprehensive testing strategies including unit tests, FHIR validation, and data consistency checks
• Contributing to schema management and evolution strategies for healthcare data transformation
• You’re the missing piece of the puzzle
• Strong experience with Apache Pyspark and/or Flink or similar distributed stream processing frameworks (Kafka Streams, Apache Storm)
• Knowledge of FHIR R4 standards and healthcare data interoperability concepts
• Experience with Python data processing libraries (Pandas, PyArrow) and dataframe abstractions like Ibis
• Proficiency in SQL and understanding of complex data transformations and joins
• Experience with Apache Kafka for real-time data streaming and event-driven architectures
• Hands-on experience with containerization (Docker) and Kubernetes orchestration
• Understanding of healthcare data sources (EMRs, laboratory systems, clinical devices) and clinical workflows
• Experience with Google Cloud Platform services, particularly BigQuery, Cloud Storage, and GKE
• Strong analytical and problem-solving skills with attention to data quality and clinical accuracy
• Experience with modern development practices including Git, CI/CD, and automated testing
• Ability to work independently and collaboratively in a fast-paced healthcare technology environment
• Great-to-haves
• Experience with PyFlink (Python API for Apache Flink) and Flink SQL
• Knowledge of FHIR/HL7 standards, clinical terminologies (SNOMED, LOINC, ICD-10), and healthcare data governance
• Experience with Pixi environment management and modern Python dependency management
• Familiarity with Apache Iceberg or other modern data lake storage formats
• Experience with Flink Kubernetes Operator and cloud-native deployment patterns
• Knowledge of healthcare compliance requirements (HIPAA, PIPEDA) and data privacy considerations
• Experience with monitoring and observability tools for distributed systems (Prometheus, Grafana)
• Background in clinical informatics, health information management, or biomedical engineering
• Experience with EMR systems like MedAccess, Epic, or Cerner
• Familiarity with Palantir Foundry, Databricks or similar data integration platforms
• Understanding of real-time vs. batch processing trade-offs in healthcare data systems.