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 (Google Cloud Platform) / BigQuery, we want to hear from you.

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).
• Google Cloud Platform Ecosystem: Strong Google Cloud Platform 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.