Greetings Position :- Google Cloud Platform Data Engineer (Health Care Background Must) Location: Across USA any Location 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. Thanks Please share resume on | 
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