Data Product Steward — Data Office

Were looking for a Data Product Steward whos 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 agents 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. Youll 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.

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

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

On-site role4 days per week in our Montreal office with 1 day of flexibility.

Unlimited vacation 3 personal days.

Annual bonus and salary review.

$1500 training budget to fuel your growth.

RRSP matching (3% company contribution).

Comprehensive health insurance.

Subsidized public transportation (Opus & Cie).

Note: This role is not remoteapplicants must be based in Montreal.

TS Imagine builds the technology the worlds 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 clients own data. Headquartered in New York with 13 offices worldwide.