Google Cloud Data Architect - AI-Ready Data Platforms

Slalom; Washington (District of Columbia); Hybrid; USD 115,000 - 146,000; Full time

Benefits offered by this job

Hybrid work environment

Well-being reimbursement

Competitive compensation

Job summary

Slalom is seeking a Data Architect for its Google COE to design and deliver enterprise-scale Google Cloud data platforms. You will architect solutions using BigQuery, Dataplex, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Vertex AI, Looker, and more, guiding migrations and modernizations across client environments.

You will collaborate with cross-functional teams, facilitate design sessions, and communicate trade-offs, risks, and roadmaps while staying ahead of cloud-native data engineering

Qualifications

• Experience designing enterprise-scale data platforms on Google Cloud.
• Strong knowledge of data governance, metadata, and data quality patterns.
• Ability to lead architecture discussions with clients and internal teams.
• Experience migrating and modernizing data estates on cloud platforms.

Responsibilities

• Lead analysis, architecture, design, and delivery of the client’s Google Cloud Data Platform solution.
• Architect solutions using BigQuery, Dataplex, Dataflow, Pub/Sub, and other GCP services.
• Collaborate with Slalom and client teams to drive consensus and delivery.
• Support pre-sales activities and define technical solution options when time allows.

Skills

GCP Data Architecture

Enterprise Data Platform Design

Cloud Platform Experience

Mentoring engineers

Client engagement

Tools

BigQuery

Dataplex

Dataflow

Dataproc

Pub/Sub

Cloud Storage

Vertex AI

Looker

GKE

CI/CD

Terraform

Job description

Slalom is seeking a Data Architect for its Google COE to design and deliver enterprise-scale Google Cloud data platforms. You will architect solutions using BigQuery, Dataplex, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Vertex AI, Looker, and more, guiding migrations and modernizations across client environments.

You will collaborate with cross-functional teams, facilitate design sessions, and communicate trade-offs, risks, and roadmaps while staying ahead of cloud-native data engineering