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.#(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 Big

Query analytical workloads
Documentation & Governance: Create source-to-target mappings, transformation rules, data contracts, lineage documentation, and business definitions.

Define transparent 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
**Requirements**
Experience: Robust 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
**We offer**
Extended Healthcare with Prescription Drugs, Dental and Vision, and Healthcare Spending Account (Company Paid)
Life and AD&D; Insurance (Company Paid)
Employee Assistance Program (Company Paid)
Telehealth (Company Paid)
Short-term Disability (Company Paid)
Long-Term Disability
Paid Time Off (including vacation and sick days)
Registered Retirement Savings Plan (RRSP) with Company match
Maternity/Parental/Adoption Leave Top-up
Employee Stock Purchase Program
Critical Illness Insurance
Employee Discounts
Unlimited access to Linked

In learning solutions
EPAM Systems, Inc. is an equal opportunity employer.

We recognize the value of diversity and inclusion in creating success for our customers, business partners, shareholders, employees and communities.

We are committed to recruiting, hiring, developing and promoting employees without discrimination.

As a global employer, this commitment includes complying with all laws in the countries in which we operate.

Nevertheless, we believe equal employment practices should not be limited to what the law requires.

Equal opportunity and inclusion are essential to motivate, empower and recognize the best in everyone.

At EPAM, employment actions are based on individual qualifications, without regard to race, color, religion, creed, gender, pregnancy status, sexual orientation, gender identity, gender expression, marital or familial status, national origin, ancestry, genetics, age, disability status, veteran status, citizenship status when otherwise legally able to work, or any other characteristic protected by law.

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