Job Title: Google Cloud Platform Data Engineer (Healthcare Domain)
Location: Remote
Duration: Fulltime
Visa: Any Independent Visa
Primary Skills Google Cloud Platform Data Stack (BigQuery, Dataflow, Composer, GCS, Pub/Sub, Dataproc, Dataplex), Healthcare Data Standards (HL7, FHIR, CCD/C-CDA, DICOM, EHR/EMR, LIS, RIS/PACS), Data Engineering & Pipelines (ETL/ELT), Data Modeling & SQL, Data Governance, HIPAA & Security Compliance
Experience 10+
Job Summary
We are looking for an experienced Google Cloud Platform Data Engineer with strong Healthcare domain expertise. The ideal candidate will architect and build enterprise-grade data platforms on Google Cloud, including BigQuery-based Data Lakes and Data Warehouses. This role requires deep hands-on experience in ingestion, transformation, modeling, governance, and cloud architecture, along with strong knowledge of clinical healthcare data standards.
Key Responsibilities
1. Data Lake & Data Platform Architecture (Google Cloud Platform)
Architect enterprise Google Cloud Platform-based data lakehouse using BigQuery, GCS, Dataflow, Dataproc, Pub/Sub, Composer, BigQuery Omni.
Define ingestion, curation, hydration, and enrichment strategies for structured/unstructured datasets.
Build canonical models and analytics-ready datasets for BI, AI/ML, and operational consumption.
Design federated data layers and self-service data products.
2. Data Ingestion & Pipelines
Architect batch, near-real-time, and streaming pipelines using Dataflow, Pub/Sub, Dataproc.
Set up ingestion for clinical datasets (HL7, FHIR, CCD, DICOM, EHR/EMR, LIS, RIS/PACS).
Build pipelines for ERP, HR, finance, supply chain systems.
Architect IoMT ingestion from medical devices, wearables, RPM systems.
Manage hybrid pipelines, on-prem cloud migrations, VPN/Interconnect setups.
3. Data Transformation & Enrichment
Build transformation frameworks using BigQuery SQL, Dataflow, Dataproc, dbt.
Implement bronze/silver/gold layers and healthcare data marts.
Enable metadata-driven pipelines with scalable transformation logic.
Enrich datasets using clinical, operational, device, or social determinant data.
4. Data Governance & Quality
Establish data governance framework (stewardship, ownership, policies).
Implement lineage, cataloging, and metadata tools (Dataplex, Data Catalog, Collibra).
Build data quality validations, profiling, anomaly detection, and SLA monitoring.
Ensure HIPAA compliance, PHI protection, DLP, IAM/RBAC, VPC SC, encryption, and auditing.
5. Cloud Infrastructure & Networking
Architect VPC networks, firewalls, VPN, Interconnect, hybrid connectivity.
Optimize BigQuery storage, partitioning, clustering, and performance.
Support containerized data services (Cloud Run, GKE).
6. Stakeholder Collaboration
Work with clinical, business, research, and IT teams for data requirements.
Collaborate with architects, security, and platform engineering teams.
Provide technical guidance to data engineering teams.
7. Hands-On Leadership
Build pipelines, transformations, POCs, and validate architecture.
Mentor junior engineers on cloud-native best practices.
Required Skills & Qualifications
Technical (Must-Have)
10+ years in Data Engineering, Architecture, or Platform Engineering.
Strong experience in Google Cloud Platform (BigQuery, Dataflow, Composer, GCS, Pub/Sub, Dataproc, Dataplex).
Deep knowledge of clinical data standards (HL7 v2.x, FHIR, CCD, DICOM).
Experience with IoT/IoMT ingestion (wearables, patient monitoring devices).
ERP dataset experience (Workday, Oracle, PeopleSoft, Lawson).
Strong SQL, data modeling (3NF, Star/Snowflake, Canonical Models).
Metadata management, lineage, governance frameworks.
Understanding of HIPAA, PHI/PII compliance, IAM, VPC security, DLP.
Cloud & Infrastructure
Solid understanding of cloud networking, VPC, DNS, IAM, firewalling.
Experience with hybrid connectivity and on-prem to cloud migrations.
Proficiency with cloud-native data movement services
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