This is a hybrid role based in downtown Toronto, requiring 3 to 4 days of on-site collaboration per week with flexibility for remote work for the remainder of your schedule.

About Infostrux

Infostrux is one of North America’s fastest-growing data-focused consultancies. Within 18 months of our founding in 2021, Snowflake recognized us at the highest partnership tier—a reflection of our deep expertise and impact.

We help organizations unlock the value of their data by building up-to-date, cloud-native platforms that power analytics, AI, and machine learning. Our work spans the modern data stack—covering data engineering, architecture, integration, and modelling with Snowflake as one of our core partners.

From startups to Fortune 500s across industries like finance, health, gaming, and retail, we design and deliver scalable data solutions that enable faster, smarter, data-driven decisions. And as proud recipients of the Great Place to Work® Certification (2026-2027), we are equally committed to fostering a culture of trust, collaboration, and growth for our people.

Job Summary

As a Principal Data Architect, FSI, you will own the data architecture on engagements with financial services clients across banking, insurance, capital markets, and wealth management, embedded with their business and technology teams.

Demand for analytical products is outpacing client platforms. You’ll build both the products and the access, security, and delivery foundation that makes them trustworthy, deciding how regulated data is modelled, who sees what, and how it flows to decision-makers.

This is a financial services role first. A wrong figure is a client conversation, not a bug ticket. Entitlements are designed in from day one.



Messy source data gets reconciled, not papered over. And every decision becomes audit evidence.

You’ll lead project teams assembled per engagement, setting technical direction and carrying proven patterns forward

What you’ll get to do

- Client-facing and executive reporting
- Architect reporting products drawn directly from core operational and front office systems, replacing spreadsheet-assembled deliverables
- Design the calculation and what-if modelling layers on top, covering performance, exposure, allocation, profitability, and scenario modelling
- Build reporting that reconciles across sources, including benchmarking and peer comparison where the business needs external context
- Self-service analytics for business teams
- Give analysts and business users governed direct access to the warehouse so they stop queuing behind data requests
- Design semantic and metric layers so key figures mean the same thing in every tool
- Support board-level reporting and scenario planning built on the same governed definitions
- Data-informed workflow optimization
- Rewire client and product onboarding onto one clean, governed data flow
- Restructure recurring operating cycles and their reporting around timely data rather than manual assembly
- AI-supported workflows




- Design the data and retrieval foundations for AI applied where the reading and preparation burden is heaviest, including due diligence, document deep dives, legal and transaction review, meeting preparation, and ongoing monitoring
- Establish how unstructured material is ingested, chunked, secured, and cited so AI output is traceable back to source
- Foundational architecture, access, and security
Own the access and security layer everything above depends on: RBAC and entitlement strategy, identity management, row-level and column-level security, and data architecture validation
- Establish DEV and PROD separation, API-based pipeline frameworks, code release automation, and security hardening as standard practice on the engagement
- Design product-level cost attribution, monitoring, and alerting so consumption is understood and controlled per analytical product
- Produce architecture decision records and control evidence that stand up to audit and client due diligence
- Leadership and delivery
- Analyze requirements with both technical and purely business-oriented stakeholders, and present proposed solutions credibly to business and finance leadership
- Lead implementation efforts, oversee data engineers, and guarantee solution quality from the data architecture point of view
- Suggest reusable patterns and process improvements to be applied across the organization
- Plan tasks for self and others, participate in leading engagements, and mentor other engineers

Must-haves

- Financial services delivery experience. You've built production data solutions inside banking, insurance, capital markets, or asset and wealth management, and you can speak to what changed because of the domain <

📌 Principal Data Architect, FSI (Toronto)
🏢 Infostrux
📍 Toronto