GCP Data Engineer (Health Care Background Must)

Summary

Strong experience architecting enterprise data platforms on Google Cloud (GCP). The architect will work as a strategic technical partner to design and build a GCP BigQuery-based Data Lake & Data Warehouse ecosystem. The role requires deep hands-on expertise in data ingestion, transformation, modeling, enrichment, and governance, combined with a strong understanding of clinical healthcare data standards, interoperability, and cloud architecture best practices.

Key Responsibilities:

1. Data Lake & Data Platform Architecture (GCP)

• Architect and design an enterprise-grade GCP-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.

2. Data Ingestion & Pipelines

• Architect batch, near-real-time, and streaming ingestion pipelines using GCP 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.

3. Data Transformation, Hydration & Enrichment

• 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.

4. Data Governance & Quality

• 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.

5. Cloud Infrastructure & Networking

• 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.

6. Stakeholder Collaboration

• 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.

7. Hands-on Leadership

• 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.

Required Skills & Qualifications

Technical Skills (Must-Have)

• 10+ years in data architecture, engineering, or data platform roles.
• Strong expertise in GCP 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.

Cloud & Infrastructure

• Solid understanding of cloud networking, hybrid connectivity, VPC design, firewalling, DNS, service accounts, IAM, and security models.
• Cloud Native Data movement services
• Experience with on-prem to cloud migrations.