Data Steward (CCDB26A)

Job summary

Own stewardship for assigned financial-data domains by defining business terms, monitoring data quality and controls, investigating issues, assessing change impacts, and maintaining ownership, lineage, usage guidance, and limitations. Build Snowflake semantic layers and refine prompts for AI products, ensure AI outputs are grounded in governed data, and provide L2 support for data incidents.

Job details

Referment is working with a capital-markets technology company whose platforms help financial institutions trade, manage risk and run their financing businesses on a shared, governed data foundation. The role sits where financial-market data meets AI: the team builds the data products and intelligence that clients rely on. It is hiring a Data Steward for its Montreal office. The Role This role has two connected halves. First, you'll own end-to-end stewardship for assigned financial-data domains: defining and documenting business definitions, setting and monitoring data-quality rules, controls and KPIs, investigating issues through to root cause, and assessing the downstream impact of proposed changes before they ship. You'll maintain ownership, lineage, usage guidance and known limitations, and act as the trusted point of contact for your domains. Second, you'll shape how that data is presented to AI. You'll build and maintain semantic layers in Snowflake that give AI-driven products the structure, definitions, relationships and business context they need, apply prompt engineering to develop, test and refine prompts that improve the accuracy and consistency of AI-generated answers, and make sure AI outputs stay grounded in well-governed data. You'll also act as L2 support for data incidents within your domains, diagnosing whether the root cause lies in source data, transformation logic, business rules or semantic definitions. What We're Looking For Experience in data management, data governance or data stewardship, ideally in financial services or capital markets. Working knowledge of Snowflake or a comparable cloud data warehouse, and SQL. Experience with at least one of securities or instrument reference data, trading lifecycle data, risk or market data. The ability to translate technical data concepts into clear business language for engineers, stakeholders and clients. Desirable Interest or prior experience in prompt engineering, semantic modelling or enabling AI agents. This could suit a data steward, data governance analyst, business analyst or data-quality specialist from a bank, asset manager or market-data provider who wants to work closer to AI-driven data products. The role is based in Montreal and is on-site, with four days a week in the office and one day of flexibility; it is not remote. #Referment

What you’ll do

Own stewardship for assigned financial-data domains by defining business terms, monitoring data quality and controls, investigating issues, assessing change impacts, and maintaining ownership, lineage, usage guidance, and limitations. Build Snowflake semantic layers and refine prompts for AI products, ensure AI outputs are grounded in governed data, and provide L2 support for data incidents.

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 securities or instrument reference data, trading lifecycle data, risk data, or market data is sought, as is the ability to explain technical concepts clearly; prompt engineering, semantic modeling, or AI-agent experience is desirable.

Listed skills

• data management · Preferred
• SQL · Preferred
• prompt engineering · Preferred
• Root Cause Analysis · Preferred
• Business analysis · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Data Stewardship
• Data Management
• Data Governance
• Data Quality
• Snowflake
• SQL
• Semantic Modeling
• Prompt Engineering
• Financial Data
• Capital Markets
• Data Lineage
• Root Cause Analysis
• Incident Support
• Business Analysis
• Stakeholder Communication

Job areas

• Data & Analytics
• Technology
• Finance & Accounting
• Software

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