We are seeking a hands-on Data Architect to design and evolve the data foundation for a production AI agent platform focused on cost optimization, anomaly detection, and automated insights. You will define architecture, models, and governed datasets that enable reliable analytics and safe agent access. Responsibilities Define target data architecture across cost, usage, inventory, contract, finance, metadata, and operational domains Design conceptual, logical, and physical data models for analytical workloads Create source-to-target mappings, transformation rules, data contracts, lineage, and business definitions Establish raw, standardized, curated, and analytics-ready data layers with clear ownership boundaries Build canonical entities, domain models, semantic datasets, and reusable dimensions for reporting and agent use cases Design integration patterns for APIs, files, databases, event streams, and semi-structured sources Partner with data engineering to define ingestion, transformation, enrichment, reconciliation, and data quality patterns Collaborate with AI engineering to define safe, discoverable, read-only datasets and agent access patterns Define query and schema standards that support reliable reasoning, analytics, and recommendations Review schemas, SQL, pipelines, and integration designs for scalability, maintainability, performance, and cost efficiency Define warehouse optimization standards such as partitioning, clustering, and materialized views Establish governance standards for lineage, access control, retention, classification, quality, and freshness Provide technical leadership through architecture and code reviews across the data domain Requirements 2+ years data architecture or data engineering experience on analytics platforms Stakeholder leadership experience guiding engineers and client stakeholders Strong project ownership skills for driving architecture decisions from discovery to delivery Advanced SQL skills with hands-on cloud data warehouse experience Strong data modeling skills across conceptual, logical, physical, and dimensional models Strong Google Cloud Platform skills including BigQuery and core integration services Hands-on Python skills for data services, POCs, or API-based integrations Strong integration skills with REST APIs, batch processing, and semi-structured data Strong data governance knowledge including lineage, access control, and retention Strong data quality skills including reconciliation and schema evolution practices Strong communication skills for translating business needs into data contracts and standards Upper-Intermediate English (B2) proficiency Nice to have Google Cloud BigQuery expertise in performance tuning and cost optimization Python proficiency for building data utilities and integration prototypes Data governance experience with catalogs, metadata management, and lineage tooling Data model experience with semantic layers and domain-driven modeling Stakeholder management experience in cross-functional architecture alignment
