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) / BigQuery, we want to hear from you. Req.#1110315973 Responsibilities 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 BigQuery 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 BigQuery 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 BigQuery 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 Requirements 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 BigQuery (or similar cloud data warehouses) GCP Ecosystem: Strong GCP knowledge, including BigQuery, 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 BigQuery 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
