Data Solutions Architect (Permanent)

ON
Hybrid
Posted Oct 15, 2026
1 position

Opens an external site

Employment type: Full-time
Experience level: Senior · 5+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week
Office presence: 4 days per week

Job summary

The Data Solutions Architect will design and guide the implementation of end-to-end data flows, from source systems to final consumption by business intelligence and data science platforms. They will collaborate with cross-functional teams to ensure data solutions are secure, scalable, and aligned with regulatory requirements in the asset management industry.

Job details

CoreFactor is searching for a Data Solutions Designer on full-time/permanent basis for a client in the GTA. This role is hybrid and will require the successful incumbent to go into the office four (4) times per week in downtown Toronto. Our client is seeking a Data Solutions Designer to join their Data, Analytics & AI team in shaping data-driven solutions that enable investment insights, operational efficiency, and client reporting across our asset management business. In this role, you will help design and guide the implementation of end-to-end data flows—from source systems through to final consumption by business intelligence platforms, data science models, and third-party consumers. This includes contributing to platform architecture, data modeling, and solution review across a modern stack. You’ll collaborate with data engineers, analysts, quants, and business stakeholders to ensure that data solutions are well-architected, secure, scalable, and aligned with the needs of a fast-paced, regulated industry. Key Responsibilities: Design End-to-End Data Solutions Design robust data solutions supporting business intelligence, data processes, analytics and AI/ML initiatives. Develop technical flowcharts, data flow diagrams (DFDs), and system interaction models to visualize and validate proposed data solution designs. Define data flows from ingestion through transformation, storage, and delivery—including real-time and batch patterns. Ensure solutions support both operational and analytical use cases (e.g. dashboards, APIs, external data delivery). Collaborate Across Teams Partner with data engineering, analytics, and AI teams to evaluate and enhance solution designs. Provide guidance on architectural decisions, platform capabilities, and technology choices within our AWS and Snowflake-based ecosystem. Modeling & Metadata Design logical and physical data models to support various levels of reporting and analysis. Partner with Data Governance to help promote standards for metadata, documentation, and lineage tracking using tools like Alation, DBT and Tableau Data Catalogs. Platform & Governance Alignment Ensure all designs align with platform standards, data governance policies, and regulatory requirements relevant to asset management. Contribute to improving data product delivery patterns, reusability, and performance optimization. Review & Influence Review proposed solution designs and challenge approaches to ensure strategic alignment, maintainability, and fit-for-purpose outcomes. Act as a thought partner in refining how we deliver data, analytics, and AI capabilities to the business. Requirements Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related field (required). Master’s degree in Data Analytics, Information Systems, or Financial Engineering (preferred). Relevant certifications (e.g. Snowflake, AWS, dbt, TOGAF) are a plus. 5+ years of experience in data architecture/data solution design, or data engineering—preferably within financial services or asset management Strong understanding of data platforms and architecture, including experience with: Snowflake for cloud data warehousing AWS for cloud services and data pipelines dbt for transformation and documentation Demonstrated experience designing data flows and models to support BI, reporting, and/or AI/ML use cases.

What you’ll do

Requirements

Candidates must possess a Bachelor's degree in a relevant field and at least 5 years of experience in data architecture or data engineering. Proficiency in cloud platforms like AWS and Snowflake, along with experience in data modeling and transformation tools like dbt, is required.