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Data Steward (D7BD25A)

  • Montreal, QC, Canada

Full-time

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Job details

Job type
Full-time
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Posting language
English
Posted

Original job description

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

More about this job

Responsibilities

Own stewardship for assigned financial-data domains, including business definitions, quality rules, controls, KPIs, lineage, usage guidance, issue investigation, and change-impact assessment. Build and maintain Snowflake semantic layers and refine prompts for AI products, while providing L2 support and ensuring AI outputs are grounded in governed data.

Requirements

Experience in data management, governance, or stewardship is sought, ideally in financial services or capital markets, along with working knowledge of Snowflake or a comparable cloud data warehouse and SQL. Candidates should have experience with securities or instrument reference data, trading lifecycle data, risk data, or market data, and be able to explain technical concepts clearly; semantic modeling, prompt engineering, or AI-agent experience is desirable.

Skills

  • Data Stewardship
  • Data Governance
  • Data Quality Management
  • Snowflake
  • SQL
  • Semantic Modeling
  • Prompt Engineering
  • Financial Data Management
  • Data Lineage
  • Root Cause Analysis
  • Impact Assessment
  • Incident Support
  • Business Analysis
  • Stakeholder Communication
  • AI Data Products

Visa sponsorship

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Categories

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

Keywords

  • Capital Markets
  • Financial Services
  • Data Stewardship
  • Data Management
  • Data Governance
  • Data Quality
  • Business Definitions
  • Data Controls
  • KPIs
  • Root Cause Analysis
  • Data Lineage
  • Snowflake
  • Cloud Data Warehouse
  • SQL
  • Semantic Layers
  • Semantic Modeling
  • Prompt Engineering
  • AI Agents
  • AI-Driven Products
  • Securities Reference Data
  • Instrument Reference Data
  • Trading Lifecycle Data
  • Risk Data
  • Market Data
  • L2 Support
  • Data Incidents
  • Business Rules
  • Transformation Logic
  • Impact Assessment

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