Data Product Steward — Data Office

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.

About Ts Imagine

TS Imagine builds the technology the world's most sophisticated financial institutions rely on to trade across every asset class, manage risk in real time, and run their financing businesses. Execution, order management, risk, and financing all run on one platform with the same governed data foundation and proprietary ontology, giving clients a single trusted view of their business and actionable, explainable intelligence they can defend to regulators, counterparties, and compliance teams.

Clients include global banks, asset managers, hedge funds, and prime brokers operating across equities, fixed income, FX, derivatives, and crypto. TS Imagine delivers this through TSIQ: AI-powered intelligence grounded entirely in each client's own data. Headquartered in New York with 13 offices worldwide.