Senior Data Architect

We are looking for a visionary and hands-on Senior Data Architect to lead our data modeling, integration patterns, and semantic dataset initiatives. In this role, you will be the bridge between traditional data engineering and cutting-edge AI systems. You will work side-by-side with our AI Engineers to design agent-ready datasets and Model Context Protocol (MCP) data-access patterns, while partnering with Data Engineers to establish robust, scalable ingestion, transformation, quality, and monitoring standards.

If you are passionate about multi-cloud cost, usage, resource, and financial datasets, and love architecting clean, scalable analytical engines in Google Cloud (GCP) / Big Query, we want to hear from you. Req#

• Data Architecture & Modeling:
  Define the target data architecture for multi-cloud cost, usage, resource, contract, financial, metadata, and operational datasets. Design conceptual, logical, and physical data models optimized for Big Query analytical workloads
• Documentation & Governance:
  Create source-to-target mappings, transformation rules, data contracts, lineage documentation, and business definitions. Define clear ownership boundaries across raw, standardized, curated, and analytics-ready data layers
• Semantic & Domain Modeling:
  Establish canonical entities, domain models, semantic datasets, and reusable dimensions for traditional reporting as well as modern AI-agent use cases
• Integration & Ingestion Design:
  Design integration patterns for APIs, files, databases, event streams, and semi-structured enterprise data. Ensure data is properly mapped, normalized, and reconciled
• AI & Agent Readiness:
  Partner with AI Engineers to design safe, discoverable, read-only Big Query datasets and MCP tools for agent access. Define query and schema standards that enable reliable agent reasoning, structured responses, analytics, and recommendations
• Performance & Cost Optimization:
  Review SQL queries, data pipelines, schemas, and integration designs for quality, scalability, maintainability, performance, and cost efficiency. Define Big Query best practices for partitioning, clustering, materialized views, and query optimization
• Data Governance & Leadership:
  Establish governance standards for lineage, ownership, access control, retention, data classification, quality, and freshness. Participate in architecture and code reviews, offering technical leadership across the data domain

• Experience:
  
  Strong professional background in data engineering, data architecture, analytics engineering, or backend/data platform development
• Data Modeling:
  Deep expertise in conceptual, logical, physical, dimensional, canonical, and domain-oriented data modeling
• Cloud Data Warehousing:
  Advanced SQL skills and hands-on experience designing large-scale analytical datasets, data marts, and multi-source integration pipelines, specifically using Google Cloud Big Query (or similar cloud data warehouses)
• GCP Ecosystem:
  Strong GCP knowledge, including Big Query, Cloud Storage, IAM, service accounts, Pub/Sub, and GKE integration patterns
• Mapping & Specifications:
  Proven ability to produce source-to-target mappings, transformation specifications, data contracts, and lineage documentation
• Performance Tuning:
  Expertise in Big Query partitioning, clustering, performance tuning, and cost-efficient query design
• Programming & Integration:
  Hands-on Python experience for data services, Proofs of Concept (POCs), transformations, or API integrations. Experience with REST APIs, event-driven integration, batch processing, and semi-structured data
• Governance & Security:
  Strong understanding of data governance, data quality, reconciliation, schema evolution, and least-privilege access patterns
• Collaboration & Review:
  Experience conducting architecture reviews, mentoring engineers, and driving consensus across cross-functional technical teams