[Remote] Google Cloud Platform Data Engineer (Health Care) with Python/Pyspark Coding Must_REMOTE@Full Time

Descrição da Vaga

Requisitos

• 10+ years in data architecture, engineering, or data platform roles
• Strong expertise in Google Cloud Platform data stack (BigQuery, Dataflow, Composer, GCS, Pub/Sub, Dataproc, Dataplex)
• Hands-on Experience With Data Ingestion, Pipeline Orchestration, And Transformations
• Deep understanding of clinical data standards: HL7 v2.x, FHIR, CCD/C-CDA
• DICOM (for scans and imaging)
• LIS/RIS/PACS data structures
• Experience with device and IoT data ingestion (wearables, remote patient monitoring, clinical devices)
• Experience With ERP Datasets (Workday, Oracle, Lawson, PeopleSoft)
• Strong SQL and data modeling skills (3NF, star/snowflake, canonical and logical models)
• Experience With Metadata Management, Lineage, And Governance Frameworks
• Solid understanding of HIPAA, PHI/PII handling, DLP, IAM, VPC security
• Solid understanding of cloud networking, hybrid connectivity, VPC design, firewalling, DNS, service accounts, IAM, and security models
• Cloud Native Data movement services
• Experience with data governance frameworks
• Experience with data quality frameworks for validation, profiling, anomaly detection, and SLA monitoring

Responsabilidades

• The role involves architecting and building a Google Cloud Platform-based data lake and data warehouse ecosystem, requiring expertise in data ingestion, transformation, and governance
• Architect and design an enterprise-grade Google Cloud Platform-based data lakehouse leveraging BigQuery, GCS, Dataproc, Dataflow, Pub/Sub, Cloud Composer, and BigQuery Omni
• Define data ingestion, hydration, curation, processing and enrichment strategies for large-scale structured, semi-structured, and unstructured datasets
• Create data domain models, canonical models, and consumption-ready datasets for analytics, AI/ML, and operational data products
• Design federated data layers and self-service data products for downstream consumers
• Architect batch, near-real-time, and streaming ingestion pipelines using Google Cloud Platform Cloud Dataflow, Pub/Sub, and Dataproc
• Set up data ingestion for clinical (EHR/EMR, LIS, RIS/PACS) datasets including HL7, FHIR, CCD, DICOM formats
• Build ingestion pipelines for non-clinical systems (ERP, HR, payroll, supply chain, finance)
• Architect ingestion from medical devices, IoT, remote patient monitoring, and wearables leveraging IoMT patterns
• Manage on-prem cloud migration pipelines, hybrid cloud data movement, VPN/Interconnect connectivity, and data transfer strategies
• Build transformation frameworks using BigQuery SQL, Dataflow, Dataproc, or dbt
• Define curation patterns including bronze/silver/gold layers, canonical healthcare entities, and data marts
• Implement data enrichment using external social determinants, device signals, clinical event logs, or operational datasets
• Enable metadata-driven pipelines for scalable transformations
• Establish and operationalize a data governance framework encompassing data stewardship, ownership, classification, and lifecycle policies
• Implement data lineage, data cataloging, and metadata management using tools such as Dataplex, Data Catalog, Collibra, or Informatica
• Set up data quality frameworks for validation, profiling, anomaly detection, and SLA monitoring
• Ensure HIPAA compliance, PHI protection, IAM/RBAC, VPC SC, DLP, encryption, retention, and auditing
• Work with cloud infrastructure teams to architect VPC networks, subnetting, ingress/egress, firewall policies, VPN/IPSec, Interconnect, and hybrid connectivity
• Define storage layers, partitioning/clustering design, cost optimization, performance tuning, and capacity planning for BigQuery
• Understand containerized processing (Cloud Run, GKE) for data services
• Work closely with clinical, operational, research, and IT stakeholders to define data use cases, schema, and consumption models
• Partner with enterprise architects, security teams, and platform engineering teams on cross-functional initiatives
• Guide data engineers and provide architectural oversight on pipeline implementation
• Be actively hands-on in building pipelines, writing transformations, building POCs, and validating architectural patterns
• Mentor data engineers on best practices, coding standards, and cloud-native development