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.#(phone hidden)

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-productive 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

📌 Senior Data Architect (Canada)
🏢 EPAM Systems
📍 Canada