GCP Cloud Data Architect ( Google Cloud Query

Location:

Wilmington, DE or New Jersey – Day 1 onsite

Duration: 12 months

Job: GCP Cloud Data Architect – responsible for end‑to‑end data architecture, design, and governance of the Google Cloud Big Query platform, ensuring scalable, secure, and cost‑effective analytics solutions.

• GCP Professional Data Engineer certification
• 12–16 years’ experience in data architecture and cloud services

• Architect strategy: design and implement a scalable, cost‑optimized Big Query architecture leveraging advanced features such as partitioning, clustering, and materialized views to maximize performance and minimize costs.
• Data modeling: develop robust enterprise data models and semantic layers to support comprehensive analytics, reporting, business intelligence, marketing campaign optimization, and advanced data science use cases.
• Business engagement: act as a trusted advisor to executives and business teams, translating business objectives into technical solutions that drive measurable outcomes.
• Data management: define and implement master and reference data strategies to ensure data consistency, accuracy, and governance across multiple domains.
• Data quality automation: lead initiatives to automate data quality assurance by creating business transformation rules, automated checks, and reconciliation frameworks to uphold high data integrity standards.
• Testing & validation: collaborate with QA teams to review and validate test cases, ensuring full coverage of business use cases and requirements, and rigorously assess data quality results.
• Integration & deployment: coordinate across multiple teams to manage system dependencies, validate integration points, and support seamless system deployment activities.
• UAT and production rollouts: work closely with business and IT stakeholders to coordinate UAT signoffs and oversee production cutovers, ensuring smooth deployment with well‑defined fallback and contingency plans.
• Governance & security: establish and enforce data governance standards including IAM role configurations, encryption policies, metadata management, and data lineage tracking.
• Cost & performance optimization: continuously monitor Big Query usage and performance metrics to recommend and implement strategies for query optimization and storage‑lifecycle policies to balance performance with cost efficiency.

• Enterprise Data Architecture Blueprint covering the full data lifecycle from ingestion through to consumption.
• Comprehensive Data Models reflecting enterprise needs and governance standards.
• Integration & Deployment Plans with dependency tracking.
• UAT and Production Rollout Strategy aligned with business and IT needs.

Seniority level:
Mid‑Senior

Employment type:

Contract