Cloud Data Architect

Location

(Wilmington DE or New Jersey)

The Cloud Data Architect will serve as the key technical leader responsible for the comprehensive 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

Exp: 10-16

• Data Architect - Architectural 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
• User Acceptance Testing (UAT) & 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.