Data Architect

The Role

The successful Candidate will serve as the technical authority on data strategy, modeling, and governance, acting as the primary bridge between Enterprise Architecture and engineering execution. The successful candidate will define concrete architectural patterns, and assist with their application, ensuring the evolution of our data assets is built on a secure, scalable, and compliant foundation.  The role will entail:  

• Strategic Architecture & Modernization: Lead the architectural design for decoupling data from our monolithic systems into domain-specific services. You will define patterns for data synchronization and consistency in a distributed environment.

• Analytics & Pipeline Architecture: Architect and oversee the evolution of our data pipeline, ensuring seamless replication of transactional data into our domain service and analytic environments. You will architect solutions with future use-cases in mind, such as advanced BI analytic dashboards, RAG systems, and AI model training.

• DataOps: Collaborating with Platform Engineers to establish DataOps best practices to ensure data reliability.

• Data Pipeline Productization: Champion a "Data-as-a-Product" mindset for our ingestion and transformation pipelines. You will oversee the evolution of our Data Pipeline and Analytic environment, ensuring these pipelines are not just internal utilities but robust, governed, and marketable assets capable of supporting external integrations and client-facing analytics.

• Cloud Data Optimization (FinOps): Architect for cost-efficiency in the cloud. You will design partition strategies, indexing, and lifecycle policies for Data Management Systems we employ to manage the costs of high-volume data storage and compute.

• Master Data Management (MDM): Define and enforce strategies for Master Data to resolve "Data Proliferation" issues. You will identify Systems of Record (SoR) and govern data flow between the core book-of-records and downstream applications.

• Security & Compliance by Design: Embed regulatory requirements (CIRO, CSA, GDPR) directly into the data model. You will ensure strict adherence to data sovereignty (residency) requirements and implement "Privacy by Design" principles to protect PII.

• Data Governance Leadership: Championing and co-leading of the Univeris Data Governance Framework. You will operationalize policies for Metadata Management, Master Data, Data Security, and Data Quality, acting as the primary facilitator for the Data Governance Office (DGO).

• Data Quality & Lifecycle: Establish automated Data Quality rules and SLAs for Critical Data Elements (CDEs) such as KYC and Account Information. You will also design retention and disposal mechanisms to meet audit and regulatory mandates.

• Collaboration: Guide engineering teams on optimal data access patterns and database performance tuning for high-volume OLTP and batch processing workloads.

• Technical Expertise:  Set the technology and security vision for a product-aligned architecture roadmap, focused on scalable data systems (e.g., databases, data warehouses, big data systems), platforms, and infrastructure for various analytics and business applications.  Leverage knowledge of several Databse technologies (SQL, NoSQL) to provide guidance on optimal data structure and/or schema design

Skills

The successful candidate will possess a blend of deep technical expertise and strategic governance acumen.

• Database Expertise: Expert-level proficiency in Microsoft SQL Server (OLTP optimization, T-SQL) and PostgreSQL. Familiarity with Google BigQuery or similar cloud data warehouses for analytics.  

• Data Modeling: Advanced skills in conceptual, logical, and physical data modeling for both relational and NoSQL paradigms. Experience designing schemas that support complex financial relationships (Accounts, Clients, Holdings).  

• Domain-Driven Design: Experience with domain-driven service architectures, and defining patterns required to maintain data consistency in a distributed system. 

• Integration & Pipelines: Experience designing data pipelines and integration patterns (CDC, Event-Driven Architecture, API-based access).  Experience with ELT tools (Dagster, AIrbyte, dbt), and familiarity with batch orchestration tools (Spring Batch, Spring Cloud Dataflow) are highly desirable.

• Cloud: Familiarity with cloud data platforms, (e.g. Google GCP Data Platform - Cloud Storage/Cloud SQL, Dataflow/Cloud Composer, BigQuery/Dataproc,etc)

• Application Knowledge: Ability to "speak the language" of developers; understanding of how Java and .NET applications interact with databases via ORMs (Hibernate/JPA, Entity Framework).

• Governance Tooling: Proficiency with Data Cataloging tools, Data Quality suites, and Metadata management platforms to support the "Clarity Through Documentation" principle.

• Evangelism: The ability to champion data culture and convince stakeholders that "Data is a Shared Enterprise Asset".

• Communication: Capable of translating complex technical data concepts into business value for Executive Sponsors and Product Managers.

Qualifications

The successful candidate will have the following qualifications and experience: 

• Experience: Minimum 7-10 years of experience in Data Architecture, Data Engineering, or a related senior technical role.

• Industry Background: Proven experience in Wealth Management, Fintech, or Banking is highly desirable. You must understand the regulatory landscape (CIRO/IIROC, MFDA, GDPR) and concepts like KYC, SoR, and PII protection.

• Governance Track Record: Demonstrated success in implementing or managing a Data Governance program. You should be able to describe how you moved an organization from "poor documentation" to a governed state.

• Architectural Evolution: Experience guiding organizations through a Monolith-to-Microservices decomposition strategy, specifically addressing the challenges of distributed data management.

Extras (Nice to Have)

• AI & ML Readiness: Experience architecting data layers for AI/ML adoption, including Vector Databases, RAG (Retrieval-Augmented Generation) patterns, or feature stores.

• Cloud Certification: Certifications in Azure or Google Cloud Platform (GCP) related to data architecture.

• Reporting Modernization: Experience transitioning legacy reporting into modern BI tools backed by a consolidated analytics platform.

• Standards and Frameworks: Knowledge of DAMA-DMBOK and BIAN's standardized data models.