Data Architect

Univeris

. Serve as the technical authority on data strategy, modeling, and governance .

Posted 9/18/2026full-timeToronto • CanadaSeniorLead💰 CA$130,000 - CA$150,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Architecture and Engineering, with a strong focus on Data Governance, Data Modeling, and the implementation of DataOps best practices. Proficient in designing scalable data pipelines and architectures that align with regulatory requirements and business objectives.

Highest-signal resume keywords

Data ArchitectureData GovernanceMicrosoft SQL ServerData Pipeline DesignRegulatory Compliance

ATS Keywords

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Hard Skills

Data ModelingT-SQLPostgreSQLEvent-Driven ArchitectureData Quality ManagementOLTP OptimizationData Synchronization PatternsMaster Data ManagementData CatalogingAPI-Based Access

Soft Skills

Technical AuthorityCommunicationLeadership

Tools & Technologies

Google BigQueryDagsterAirbyteSpring BatchCloud SQLDataflowCloud ComposerBigQueryDataprocHibernate

Certifications & Qualifications

Azure Data Architecture CertificationGCP Data Architecture Certification

Industry Keywords

Wealth ManagementFintechBankingCIROGDPRKYCSystems of RecordData SovereigntyPrivacy by DesignDAMA-DMBOK

Tech Stack

Tools & technologies

AzureBigQueryCloudEntity FrameworkGoogle Cloud PlatformHibernateJavaMicroservicesMS SQL ServerNoSQLPostgresSpringSQL.NET

About the role

Key responsibilities & impact

Serve as the technical authority on data strategy, modeling, and governance
Act as the bridge between Enterprise Architecture and engineering execution
Define architectural patterns and assist with their application
Lead the design for decoupling data from monolithic systems into domain-specific services
Define data synchronization and consistency patterns for distributed environments
Architect and oversee data pipelines and replication into domain-service and analytics environments
Design for advanced BI dashboards, RAG systems, and AI model training
Establish DataOps best practices with Platform Engineers
Champion a Data-as-a-Product approach for ingestion and transformation pipelines
Oversee the evolution of data pipeline and analytics environments for external integrations and client-facing analytics
Design cloud cost-efficiency strategies, including partitioning, indexing, and lifecycle policies
Define Master Data Management strategies, identify Systems of Record, and govern data flows
Embed CIRO, CSA, GDPR, data sovereignty, and Privacy by Design requirements into data models
Co-lead the Univeris Data Governance Framework and facilitate the Data Governance Office
Operationalize metadata management, master data, data security, and data quality policies
Establish automated data quality rules and SLAs for Critical Data Elements such as KYC and account information
Design retention and disposal mechanisms for audit and regulatory compliance
Guide engineering teams on data access patterns and database performance tuning for OLTP and batch workloads
Set the technology and security vision for a scalable, product-aligned data architecture roadmap
Report to the Director of Enterprise Architecture

Requirements

What you’ll need

Minimum 7–10 years of experience in Data Architecture, Data Engineering, or a related senior technical role
Proven experience in Wealth Management, Fintech, or Banking is highly desirable
Understanding of the regulatory landscape (CIRO/IIROC, MFDA, GDPR), KYC, Systems of Record (SoR), and PII protection
Demonstrated success implementing or managing a Data Governance program
Experience guiding organizations through Monolith-to-Microservices decomposition, including distributed data management
Expert-level proficiency in Microsoft SQL Server, OLTP optimization, and T-SQL
Proficiency in PostgreSQL
Familiarity with Google BigQuery or similar cloud data warehouses
Advanced conceptual, logical, and physical data modeling skills for relational and NoSQL paradigms
Experience with domain-driven service architectures and distributed data consistency patterns
Experience designing data pipelines and integration patterns, including CDC, Event-Driven Architecture, and API-based access
Experience with ELT tools such as Dagster, Airbyte, and dbt
Familiarity with batch orchestration tools such as Spring Batch and Spring Cloud Dataflow is highly desirable
Familiarity with Google Cloud data platforms, including Cloud Storage, Cloud SQL, Dataflow, Cloud Composer, BigQuery, and Dataproc
Understanding of Java and .NET database interactions through Hibernate/JPA and Entity Framework
Proficiency with data cataloging tools, data quality suites, and metadata management platforms
Required ability to translate complex technical data concepts into business value
Nice-to-have: AI/ML data layers, Vector Databases, RAG patterns, feature stores, Azure or GCP data architecture certifications, modern BI tools, DAMA-DMBOK, and BIAN standardized data models

Benefits

Comp & perks

Full-time, permanent employment
Hybrid work arrangement
Office accommodation for persons with disabilities during the application process