Google Cloud Platform Data Engineer (Healthcare Domain Expertise)

Summary: Strong experience architecting enterprise data platforms on Google Cloud (Google Cloud Platform). The architect will work as a strategic technical partner to design and build a Google Cloud Platform 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 (Google Cloud Platform)

• 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.

2. Data Ingestion & Pipelines

• 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.

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 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.

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.