Your Newpany Our client is a global miningpany dedicated to safely producing nickel, copper, cobalt, and platinum group metalscritical resources powering the worlds energy transition.

With a mission to improve lives and shape a better future together, Vale Base Metals plays a vital role in modern life.

Their metals are found in everyday technologies, lifesaving medical equipment, and electric vehicles advancing climatechange solutions.

With more than 15,000 employees worldwide, the organization operates across Canada, Brazil, Indonesia, the United Kingdom, and Japan.

They aremitted to transforming critical minerals into longterm prosperity and sustainable developmentmaking this an opportunity to contribute to work that truly matters.

Your Recent Role You are a senior, detail-oriented data professional with strong technical expertise in data quality, dataernance, data cataloguing, stewardship, privacy, and security.

You thrive inplex environments, work with minimal supervision, and are passionate about delivering reliable, high-quality data solutions end to end.

You bring strong analytical thinking, collaboration skills, and the ability to translate business requirements into scalable technical solutions.

You arefortable working in cross-functional teams and enjoy continuous learning in modern data technologies.

In This Role, You Will: Data Quality & Development Design, build, and maintain data quality pipelines for profiling, validation, cleansing, enrichment, and monitoring Independently define, configure, deploy, test, and operationalize enterprise data quality rules, thresholds, scorecards, and controls Develop exception handling, remediation workflows, root-cause analysis processes, and issue management mechanisms to improve data quality oues.

Automate data quality checks to improve reliability, reduce manual effort, and support production operations Optimize performance, scalability, resiliency, and fault tolerance of data quality solutions Master Data Management (MDM) Design and implement MDM domain models, canonical data models, hierarchies, reference data structures, match/merge rules, and survivorship logic Configure MDM platforms, onboarding workflows, mastering processes, golden record design, and integrations across source and consuming systems.

Develop ingestion, validation, stewardship, and publication pipelines to support trusted master data across systems Establish data standards, crosswalks, deduplication strategies,



and data quality controls aligned toernance requirements for critical master data domains Support issue resolution, survivorship tuning, hierarchy maintenance, and ongoing operational support for enterprise MDM solutions Partner with data stewards andernance teams to establish stewardship workflows, ownership models, critical data elements, business glossaries, andernance controls Build integrations between DQ/MDM/catalogue platforms and enterprise systems, APIs, data lakes, and cloud data platforms Implement and support data cataloguing capabilities including metadata harvesting, lineage integration, glossary curation, classification, and tagging Support cloud-based data environments (e.g., Azure) and ensure secure,pliant, anderned data movement across platforms Apply data security techniques such as tokenization, detokenization, masking, encryption, and access controls for sensitive and regulated data.

Monitor system performance, scalability, and operational health of data quality andernance solutions Dataernance &pliance; Implement and enforce dataernance policies, standards, controls, and operating model requirements across critical data domains Apply data privacy principles and regulatory requirements by supporting sensitive data identification, classification, retention, minimization, andpliant handling practices.

Monitor data quality KPIs, issue trends, and control effectiveness, and implement alerting and remediation mechanisms Maintain metadata, lineage, audit trails,ernance artifacts, andpliance documentation to support transparency and traceability Deliver trusted datasets for reporting, analytics, and AI use cases Ensure data integrity across dashboards and reporting environments Guide teams on best practices for consuming curated data Stakeholder Collaboration Translate business requirements into technical data solutions Partner with data architects,ernance teams, and business SMEs Management & Training Create documentation, training materials, and knowledge-sharing sessions Support adoption of DQ/MDM tools and processes across the organization Drive continuous improvement initiatives Education & Certifications: Bachelors degree inputer science,



Data Science, Software Engineering, or a related field Certification in Data Management (e.g., DAMA) preferred Cloud certifications (Azure) and data engineering certifications preferred Experience: 8+ years of experience in data development, data quality, dataernance, or data management Strong experience designing, configuring, and implementing enterprise data quality and MDM solutions from requirements through production support Experience implementing data cataloguing, metadata management, glossary, lineage, and stewardship workflows in enterprise environments Experience working with privacy, data protection, and security controls for sensitive data, including masking, tokenization, and access management Proven ability to operate as a senior hands-on individual contributor, independently driving technical design and implementation with minimal supervision Strong experience collaborating with cross-functional teams including architects,ernance leads, stewards, analysts, and business stakeholders Skills: Strong knowledge of enterprise Data Quality, MDM, dataernance, data cataloguing, and data stewardship practices Proven ability to define, configure, deploy, and operationalize data quality rules, controls, scorecards, and remediation workflows Experience with data catalogue and metadata management capabilities including glossary, lineage, classification, and tagging Strong understanding of dataernance frameworks, ownership models, critical data elements, and control monitoring Knowledge of data privacy and security techniques, including tokenization, detokenization, masking, encryption, and access controls Proficiency in SQL, Python, and ETL/ELT tools Experience with Informatica (IDQ, MDM) and Azure Data Services; familiarity with catalogueernance tools such as Purview, Collibra, Atlan, or Informatica EDC is an asset.

Data modelling, integration, APIs, Dev

Ops, and metadata/lineage expertise Strong analytical, problem-solving,munication, and stakeholder engagement skills Ability to manage multiple priorities in dynamic environments while working independently and driving oues end to end What We Offer You Participation in apetitive Defined Contribution Pension package Leave for all of lifes reasons (vacation, personal, sick, parental) Work culture dedicated to safety, diversity & inclusion, and career growth Employee Family Assistance Program Virtual Healthcare online Online training and career development opportunities

📌 Specialist Data Quality Developer (Toronto)
🏢 Hays
📍 Toronto