Role Overview

We're looking for a Data Product Steward who's excited to sit at the intersection of financial-market data and AI - someone who cares as much about getting a definition exactly right as they do about seeing that definition come alive inside an AI agent's response. This role combines two closely connected responsibilities: end-to-end stewardship for assigned financial-data domains, and the development of Snowflake semantic layers and prompts that power AI-driven data products. You'll move fluidly between governing and improving data at the source and shaping how that data is structured, understood, and surfaced for AI agents and applications - helping turn complex financial data into something people and machines alike can trust.

You'll be a strong fit if you have:
• Experience in data management, data governance, or data stewardship, ideally in financial services / capital markets
• Working knowledge of Snowflake (or a comparable cloud data warehouse) and SQL
• Experience with at least one of: securities/instrument reference data, trading lifecycle data, risk or market data
• Interest or prior experience in prompt engineering, semantic modeling, or AI-agent enablement
• Strong communication skills - able to translate technical data concepts into clear business language for engineers, business stakeholders, and clients

Responsibilities
• Define and own the architecture of the agentic marketing system - infrastructure, data model, agent design, and tooling choices are yours to make
• Build and deploy LLM-based agents for lead scoring, content recommendation, and competitive monitoring, with appropriate evaluation frameworks and human-in-the-loop checkpoints where the business requires them
• Construct the data pipeline that connects CRM, intent data, web analytics, and ad platforms into a unified, continuously updating signal
• Operate the system in production: monitor performance, debug failures, retrain models, and iterate on agent logic as market and business conditions evolve
• Define and track performance metrics across all capabilities; report outcomes to the CMO and Revenue leadership
• Collaborate closely with Sales to understand what pipeline intelligence actually moves deals, and build to that signal
• Work with the broader marketing team to ensure the system integrates with campaigns, events, and content workflows

AI-Driven Data Product Development
• Build, maintain, and enhance semantic layers in Snowflake that provide the structure, definitions, relationships, and business context AI-driven products need
• Ensure data exposed through semantic layers is clearly defined, discoverable, consistent, and suitable for consumption by AI agents and applications
• Apply prompt engineering to develop, test, evaluate, and refine prompts and prompt patterns that improve accuracy, relevance, and consistency of AI-generated responses
• Translate complex financial-data concepts into semantic models, metadata, prompts, and instructions for AI-driven solutions
• Identify and close gaps in data, definitions, context, prompts, or user requirements that limit AI output quality
• Support agent reliability by ensuring AI outputs are grounded in accurate, well-governed data

Data Stewardship & Domain Ownership
• Own end-to-end stewardship for assigned data domains: definition, documentation, governance, quality control, and continuous improvement
• Establish and monitor data-quality rules, controls, and KPIs (e.g., % fields with business definitions, SLA for issue resolution, quality-score trends)
• Investigate data-quality issues, drive root-cause analysis, and coordinate resolution with engineering and product teams
• Assess impact of proposed data changes on downstream consumers before they ship; perform QA/validation prior to production release
• Maintain documentation: business definitions, ownership, lineage, usage guidance, known limitations
• Act as the trusted point of contact for assigned domains, communicating data-quality risks, changes, and limitations proactively to stakeholders and clients

L2 Support & Operations
• Serve as L2 support for data incidents, questions, and production issues within assigned domains
• Diagnose whether root cause is source data, transformation logic, business rules, or semantic definition
• Plan remediation for identified issues and communicate progress and impact clearly to related teams

WHY TS IMAGINE?
• On-site role-4 days per week in our Montreal office, with 1 day of flexibility.
• Unlimited vacation + 3 personal days.
• Annual bonus and salary review.
• $1,500 training budget to fuel your growth.
• RRSP matching (3% company contribution).
• Comprehensive health insurance.
• Subsidized public transportation (Opus & Cie).

Note: This role is not remote-applicants must be based in Montreal.