Data Steward
- Montreal, QC, Canada
$1,500 training budget to fuel your growth. · Full-time
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Job details
- Pay
- $1,500 training budget to fuel your growth.
- Job type
- Full-time
- Apply by
- Work location
- Note: This role is not remote - applicants must be based in Montreal
- Posting language
- English
- Posted
Original job description
Role Overview
We're looking for a Data 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:
AI-Driven Data Product Development
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.
We're looking for a Data 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
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
- 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
- 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 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).
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.
More about this job
Responsibilities
Own end-to-end stewardship for assigned financial-data domains, including definitions, documentation, governance, quality controls, issue resolution, and operational support. Build and improve Snowflake semantic layers and prompts so AI products and agents can deliver accurate, consistent, and well-grounded responses.
Requirements
Candidates should have experience in data management, governance, or stewardship, ideally in financial services or capital markets, along with working knowledge of Snowflake or a comparable cloud data warehouse and SQL. Experience with at least one financial-data domain and an interest or background in prompt engineering, semantic modeling, or AI-agent enablement are sought, as are strong communication skills.
Skills
- Data Stewardship
- Data Governance
- Data Management
- Snowflake
- SQL
- Semantic Modeling
- Prompt Engineering
- AI Agent Enablement
- Financial Data
- Securities Reference Data
- Trading Lifecycle Data
- Risk Data
- Market Data
- Data Quality Management
- Root-Cause Analysis
- Stakeholder Communication
Benefits
- Unlimited Vacation
- Personal Days
- Annual Bonus
- Annual Salary Review
- Training Budget
- RRSP Matching
- Health Insurance
- Subsidized Public Transportation
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Finance & Accounting
- Customer Service & Support
Keywords
- Financial Services
- Capital Markets
- Data Stewardship
- Data Governance
- Data Management
- Snowflake
- SQL
- Cloud Data Warehouse
- Semantic Layers
- Semantic Modeling
- Prompt Engineering
- AI Agents
- AI-Driven Data Products
- Securities Reference Data
- Trading Lifecycle Data
- Risk Data
- Market Data
- Data Quality Rules
- Data Quality KPIs
- Root-Cause Analysis
- Data Lineage
- Metadata
- Business Definitions
- Data Validation
- L2 Support
- Production Operations
- Data Incidents
- Financial Data
- Data Ontology