Senior Data Architect

EPAM Systems; Canada; Remote; CAD 130,000 - 180,000; Full time

Benefits offered by this job

Extended Healthcare

Dental and Vision

RRSP with Company match

Life and AD&D Insurance

Employee Assistance Program

Telehealth

Paid Time Off

LinkedIn learning access

Job summary

EPAM Systems seeks a Senior Data Architect to lead data modeling, integration patterns, and semantic datasets across multi-cloud environments, with emphasis on scalable BigQuery analytics in Google Cloud. You will collaborate with AI engineers to design agent-ready datasets and model context protocols while ensuring robust ingestion, quality, lineage, and governance across raw to analytics-ready layers.

Join a global team delivering cutting-edge data solutions and scalable analytics while

Qualifications

• Deep expertise in data modeling across conceptual/logical/physical schemas.
• Experience with canonical/domain models and semantic datasets.
• Strong knowledge of BigQuery performance tuning and cost optimization.
• Proven ability to design source-to-target mappings and data contracts.
• Experience with data governance and lineage documentation.

Responsibilities

• Define target data architecture for multi-cloud datasets.
• Design conceptual, logical, and physical models for analytics workloads.
• Create mappings, rules, and governance documentation.
• Partner with AI and data engineers to enable agent-ready datasets.
• Review and optimize SQL queries, pipelines, and cost.
• Lead architecture reviews and mentor engineers.

Skills

Data modeling

BigQuery

GCP

SQL

Python

Data governance

Architecture reviews

Leadership

Education

Bachelor's degree in Computer Science or related field

Master's degree preferred

Tools

BigQuery

Cloud Storage

Pub/Sub

GKE

IAM

Job description

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.#1110315973The recruiting efforts for this position are intended to fill an existing vacancy for a new position.ResponsibilitiesData 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 workloadsDocumentation & 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 layersSemantic & Domain Modeling: Establish canonical entities, domain models, semantic datasets, and reusable dimensions for traditional reporting as well as modern AI-agent use casesIntegration & Ingestion Design: Design integration patterns for APIs, files, databases, event streams, and semi-structured enterprise data. Ensure data is properly mapped, normalized, and reconciledAI & 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 recommendationsPerformance & 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 optimizationData 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 domainRequirementsExperience: Strong professional background in data engineering, data architecture, analytics engineering, or backend/data platform developmentData Modeling: Deep expertise in conceptual, logical, physical, dimensional, canonical, and domain-oriented data modelingCloud 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 patternsMapping & Specifications: Proven ability to produce source-to-target mappings, transformation specifications, data contracts, and lineage documentationPerformance Tuning: Expertise in BigQuery partitioning, clustering, performance tuning, and cost-efficient query designProgramming & 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 dataGovernance & Security: Strong understanding of data governance, data quality, reconciliation, schema evolution, and least-privilege access patternsCollaboration & Review: Experience conducting architecture reviews, mentoring engineers, and driving consensus across cross-functional technical teamsWe offerExtended 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 DisabilityPaid Time Off (including vacation and sick days)Registered Retirement Savings Plan (RRSP) with Company matchMaternity/Parental/Adoption Leave Top-upEmployee Stock Purchase ProgramCritical Illness InsuranceEmployee DiscountsUnlimited access to LinkedIn learning solutionsEPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our clients, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.Engineer the Future with a Career at EPAMEPAM Canada welcomes and encourages applications from candidates with disabilities. Please contact WFA Human Resource CA [e-mail] if you have questions in this regard, or if you require an accommodation to complete the application process. Click here to review EPAM’s Accessibility for Ontarians with Disabilities Accessibility Policies and Multi-Year Access.An artificial intelligence system is software that is developed with one or more techniques that can, for a given set of human-defined objectives, using algorithmic information processing, generate outputs such as content, predictions, recommendations, or decisions with varying levels of autonomy (“AI”). Tasks that humans have traditionally done by thinking and reasoning are increasingly being done by, or with the help of, AI to help create efficiencies.EPAM may use AI during the recruitment process, in connection with collecting or processing your personal data. Some (non-exhaustive) examples of tasks that EPAM may use AI for include conducting initial screening, creating transcripts of interviews, and assessing applications/CVs against defined job description criteria to make suggestions to the individuals evaluating your candidacy.Your personal data and the results of any processing are not shared with AI applications outside of EPAM infrastructure. While EPAM may use AI to help create efficiencies during the recruitment process, EPAM does not use AI to make hiring decisions, which is done by EPAM Talent Acquisition and management.