Google Cloud Platform Lead / Architect – Data Engineering

Role: Google Cloud Platform Lead / Architect – Data Engineering
Experience: 15+ Years
Location: Hartford, CT (Onsite/Hybrid)
Domain Preference: Healthcare (Preferred)

Must Have Experience

• 15+ years – Google Cloud Platform Lead / Architect with strong Data Engineering foundation
• 6+ years – IAM, VPC, GCS, BigQuery, Vertex AI, GKE, Compute Engine, GitHub Actions, Dataproc
• 6+ years – Data warehouse architecture and distributed systems
• 6+ years – Architecture design including HLD and LLD
• 6+ years – BigQuery data warehouse/lakehouse optimization and data modeling
• 6+ years – DevOps, CI/CD pipeline automation
• 6+ years – AI/ML enablement using Vertex AI

Job Summary

We are seeking an experienced Google Cloud Platform Lead / Architect with a strong Data Engineering foundation to design and deliver secure, scalable, and cost-optimized data platforms on Google Cloud Platform (Google Cloud Platform).

The ideal candidate will have hands-on experience with IAM, VPC, GCS, BigQuery, Vertex AI, GKE, Compute Engine, Dataproc, and GitHub Actions, along with expertise in data warehouse architecture, distributed systems, and DevOps practices.

Key Responsibilities

Architecture & Solution Design

• Lead end-to-end architecture for data platforms on Google Cloud Platform, including networking, security, compute, storage, and analytics components.
• Define High-Level Design (HLD) and Low-Level Design (LLD) along with architecture standards and reference patterns.
• Design frameworks for data ingestion, transformation, serving, and governance.
• Drive architecture decisions balancing performance, scalability, reliability, cost optimization, and security.
• Conduct architecture reviews, design validations, and technical audits.

Data Engineering & Data Warehousing

• Architect and implement robust data pipelines for structured, semi-structured, and unstructured data.
• Develop ETL/ELT workflows and batch/streaming data pipelines.
• Design and optimize BigQuery-based data warehouse and lakehouse architectures.
• Implement dimensional modeling, partitioning, clustering, and query performance optimization.
• Lead enterprise Data Warehouse (DWH) design, including:
• Conceptual, Logical, and Physical Data Models
• Slowly Changing Dimensions (SCD)
• Conformed dimensions
• Data quality frameworks
• Data lineage and governance

Google Cloud Platform Platform Engineering (Hands-on)

• Implement security and access management using IAM policies, service accounts, and least privilege access.
• Configure and manage VPC networking and security policies.
• Engineer workloads using GKE and Compute Engine ensuring scalability, observability, and operational readiness.
• Utilize Google Cloud Storage (GCS) for governed storage and lifecycle management.
• Leverage Dataproc for Spark/Hadoop-based distributed data processing.

DevOps / CI-CD / Automation

• Build and manage automated data pipelines using Google Cloud Platform-native services.
• Develop CI/CD pipelines using Git and GitHub Actions.
• Implement DevOps best practices, including:
• Git branching strategies
• Environment promotion workflows
• Artifact/version management
• Infrastructure automation
• Rollback and release strategies

AI / ML Enablement

• Collaborate with Data Science and ML teams to operationalize machine learning services using Vertex AI.
• Integrate training and inference pipelines with enterprise data platforms.
• Support secure AI governance frameworks including explainability, privacy controls, and audit readiness.

Required Technical Skills

Google Cloud Platform (Hands-on)

• IAM
• VPC
• Google Cloud Storage (GCS)
• BigQuery
• Vertex AI
• Google Kubernetes Engine (GKE)
• Compute Engine
• Dataproc

Data Engineering

• Advanced SQL
• Python / PySpark
• Data pipeline architecture
• Performance tuning and optimization
• Data quality frameworks

Data Warehousing

• Enterprise DWH architecture
• Dimensional data modeling
• Distributed data processing concepts
• BigQuery query optimization

DevOps & CI/CD

• Git
• GitHub Actions
• Pipeline automation
• Environment management

Good to Have Skills

• Infrastructure as Code: Terraform or Google Cloud Platform Deployment Manager
• MLOps exposure: Model lifecycle management, experiment tracking, ML CI/CD pipelines
• Monitoring and deployment automation frameworks

Domain Experience

• Healthcare domain experience preferred
• Medicare STAR Ratings experience is a strong plus

Certifications

Preferred certifications:

• Google Professional Cloud Architect
• Google Professional Data Engineer

Education

Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).