Sr. GCP Data Engineer

Galent; Canada; On-site; CAD 120,000 - 150,000; Full time

Job summary

A healthcare technology company is seeking an experienced Data Architect to design and build GCP-based data platforms. The ideal candidate will have over 10 years of experience in data architecture, strong technical skills in the GCP data stack, and a solid understanding of clinical healthcare data standards. This role involves establishing data governance, ensuring data quality, and collaborating with various stakeholders to meet business data needs. This position offers an exciting opportunity to influence data strategies in a clinical environment.

Qualifications

• 10+ years in data architecture, engineering, or data platform roles.
• Strong expertise in GCP data stack and data ingestion.
• Hands-on experience with cloud networking and HIPAA compliance.

Responsibilities

• Architect and design data platforms on GCP.
• Ensure data governance and quality across platforms.
• Collaborate with stakeholders on data use cases and models.

Skills

Data architecture

GCP data stack

Data ingestion

Clone three-tier model

SQL

Tools

BigQuery

Dataflow

Pub/Sub

Dataproc

Informatica

Job description

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.
• 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.
• Build transformation frameworks using BigQuery SQL, Dataflow, Dataproc, or dbt.
• Define curation patterns including bronze/silver/gold layers, canonical healthcare entities, and data marts.
• 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.
• 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.
• 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.
• 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.