Principal GCP Data Architect

About the Role

We are seeking a hands-on Principal GCP Data Architect to lead the design and implementation of large-scale, modern data ecosystems.

The ideal candidate is an experienced data architect with a strong track record of leading large-scale cloud migrations, building self-service data platforms, and establishing robust data governance, security, and architecture standards.

This role requires deep technical expertise in Google Cloud Platform, strong architectural leadership, and the ability to work effectively with engineering teams and senior stakeholders.

Key Responsibilities

Strategic Data Architecture

Define and evolve the long-term architecture and roadmap for enterprise data platforms on Google Cloud Platform (GCP).
Design scalable, secure, and high-performance data architectures aligned with business and technology objectives.
Establish architectural standards, principles, and best practices across data engineering and analytics environments.
Evaluate emerging technologies and recommend solutions that support evolving data and analytics requirements.

Self-Service Data Platform

Architect and oversee modern, self-service data platforms that enable distributed teams while maintaining appropriate enterprise standards.
Design platforms that support data ingestion, processing, analytics, machine learning, and data discovery.
Establish reusable architecture patterns and frameworks to accelerate data product development.
Enable engineering and analytics teams to independently consume and deliver trusted data.

Large-Scale Cloud Migration

Lead complex, end-to-end migrations of enterprise data platforms to GCP.
Develop migration strategies, target architectures, transition plans, and implementation roadmaps.
Address scalability, performance, security, data quality, and operational considerations throughout migration programs.
Drive modernization of legacy data platforms and workloads as part of cloud transformation initiatives.

Data Governance & Security

Design and implement enterprise data governance and security frameworks.
Establish appropriate controls for identity and access management, data protection, encryption, masking, and privacy.
Work with GCP security and governance capabilities including IAM, VPC Service Controls, Dataplex, and related services.
Promote automated governance, data quality, lineage, metadata management, and policy enforcement.
Ensure data platforms meet enterprise security, compliance, and operational requirements.

Infrastructure as Code & Automation

Drive infrastructure automation using Terraform, Pulumi, or similar Infrastructure-as-Code technologies.
Establish repeatable, scalable, and version-controlled infrastructure deployment practices.
Promote DevOps and automation principles across data platform engineering.
Support containerized data workloads using GKE/Kubernetes and related technologies.

Technical Leadership

Provide technical leadership and architectural guidance to large, cross-functional engineering teams.
Partner with data engineers, data scientists, analysts, platform engineers, security teams, and technology leaders.
Establish technical standards and mentor engineering teams on modern data architecture practices.
Operate effectively within Agile and DevOps environments.

Stakeholder Engagement

Communicate complex architectural concepts and trade-offs clearly to technical and non-technical stakeholders.
Work with senior technology and data leadership to define priorities, manage technical risk, and communicate the value of data platform initiatives.
Provide architectural guidance throughout major transformation programs.

Technical Requirements

Google Cloud Platform

Expert-level experience with the GCP data ecosystem, including:

Storage & Data Warehousing

BigQuery
BigLake
BigQuery Omni
Google Cloud Storage

Data Processing

Dataflow / Apache Beam
Dataproc
Apache Spark
Hadoop

Data Integration & Messaging

Pub/Sub
Apache Kafka
Confluent

Analytics & AI

Looker
Vertex AI
BigQuery ML

Modern Data Stack

Strong experience with dbt
Apache Airflow / Cloud Composer
Docker and containerization
GKE / Kubernetes
Modern data platform architecture and engineering practices

Infrastructure & Automation

Terraform, Pulumi, or equivalent Infrastructure-as-Code technologies.
Strong understanding of CI/CD and DevOps practices.
Experience designing automated and repeatable cloud infrastructure.

Experience & Qualifications

18–20 years of experience across Data Engineering, Data Warehousing, Business Intelligence, and Data Architecture.
6+ years of hands-on GCP experience.
Proven experience leading enterprise-scale, PB-level cloud migrations.
Experience leading at least two large-scale data migration or modernization programs.
Strong experience designing and implementing enterprise data platforms.
Demonstrated experience leading large, cross-functional engineering teams.
Experience working in Agile and DevOps environments.
Strong understanding of data governance, security, architecture, and cloud infrastructure.
Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related technical discipline.

Certification

Active Google Cloud Professional Data Engineer certification required.

Additional Qualification

A Data Readiness Placement (DRP) ID with a verified score of 50+ is highly preferred as evidence of advanced technical proficiency and architectural capability.

What We're Looking For

The successful candidate will be:

A hands-on technical architect who can move from strategy to implementation.
Experienced in designing and scaling enterprise data platforms.
Comfortable leading complex cloud migration and modernization programs.
Strong in GCP architecture, data engineering, governance, and security.
Able to establish self-service capabilities while maintaining enterprise standards.
Comfortable working with both engineering teams and senior technology stakeholders.
A strong technical communicator who can explain architectural decisions, trade-offs, risks, and business impact clearly.
Experienced in leading teams through large-scale technology transformation.