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Data Management and Quality Developer

Kinross • Ontario, Canada • 2w ago

Start Date ASAP

Employment Type Permanent

Hybrid Work Environment (3 days in office, 2 days remote with flexible hours)

Dress Code Business Casual

Location Downtown Toronto, Outside of Union Station (TTC & GO accessible)

A Great Place to Work

Who We Are

Kinross is a Canadian-based global senior gold mining company with operations and projects in the United States, Brazil, Mauritania, Chile and Canada. Our focus on delivering value is based on our four core values of Putting People First, Outstanding Corporate Citizenship, High Performance Culture, and Rigorous Financial Discipline. Kinross maintains listings on the Toronto Stock Exchange (symbol:K) and the New York Stock Exchange (symbol:KGC).

Mining responsibly is a priority for Kinross, and we foster a culture that makes responsible mining and operational success inseparable. In 2021, Kinross committed to a greenhouse gas reduction action plan as part of its Climate Change strategy, reached approximately 1 million beneficiaries through its community programs, and recycled 80% of the water used at our sites. We also achieved record high levels of local employment, with 99% of total workforce from within host countries, and advanced inclusion and diversity targets, including instituting a Global Inclusion and Diversity Leadership Council.

Eager to know more about us? Visit Home - Kinross Gold Corporation

Job Description

Reporting to the Manager, Data Governance, the Data Management & Quality Developer is responsible for building, automating, and maintaining the technical capabilities that ensure Kinross data is accurate, consistent, secure, and compliant throughout its lifecycle.

This role serves as the technical execution arm of the Data Governance function, with a primary focus on Data and Metadata Management, Data Quality, including the design and implementation of Data Scrambling and Masking frameworks that support regulatory, operational, and analytical objectives. The incumbent will work collaboratively with data stewards, governance stakeholders, data engineering and analytics teams to deliver trusted, well-managed, and properly protected data across Kinross’ enterprise ecosystem.

High-quality, well-governed, and properly protected data is fundamental to Kinross’ success. By developing the frameworks, monitoring capabilities, and automation that enable self-service, enhance metadata visibility, and ensure secure data handling, this role strengthens the reliability, efficiency, and compliance of Kinross’ enterprise data landscape - laying the foundation for confident decision-making and sustainable data governance maturity.

Job Responsibilities

Metadata & Data Management (30%)

Integrate, catalog, and maintain technical metadata across Kinross’ enterprise data landscape, consolidating information from data sources, ETL/ELT pipelines, and reporting systems to ensure a unified, accurate, and discoverable view of data structures, lineage, and ownership.
Develop and maintain end-to-end technical data lineage to provide transparency into how data moves, transforms, and is consumed across systems. Leverage this metadata to perform impact analysis, identify upstream and downstream dependencies, and support proactive change management and reduce data operational risk.
Support the curation and enrichment of metadata within the enterprise data catalog by augmenting it with additional context including classifications, sensitivity indicators/tags, and data quality thresholds.

Data Quality Enablement & Implementation (30%)

Support the development and operationalize data quality frameworks, rules/thresholds, and scorecards that align with data management best practices and standards such as:
- Build and maintain Business Data Quality capabilities, delivering data profiles, monitoring dashboards, and alerts for business stakeholders to related to data health and quality dimensions.
- Build and maintain Technical Data Quality capabilities, developing data load-validation dashboards, data completeness checks, and automated alerting for data engineering teams.
Support overall Data Quality Implementation, creating and documenting reusable frameworks, templates, and automated pipelines for Data Quality rule deployment and exception handling.
Partner with business and technical teams to identify root causes and drive sustainable remediation and prevention of recurring issues.

Data Scrambling & Masking (20%)

Develop, configure, and execute data scrambling and masking processes to protect sensitive and personal data across different / applicable system environments.
Implement and maintain deterministic masking logic in alignment with Kinross’ Data Privacy and Security standards.
Partner with IT and Compliance to ensure all applicable system environments contain properly protected data and meet audit requirements.

Governance Enablement & Compliance (10%)

Collaborate with data stewards, data owners, and governance councils to operationalize data management and privacy policies.
Support the curation, classification and tagging of PII and sensitive data in other various data catalogs (ie - Databricks Unity Catalog).
Support the monitoring and adherence to retention, classification, and security requirements, ensuring alignment with governance frameworks and privacy regulations.

Continuous Improvement & Automation (10%)

Enhance DQ and masking frameworks through automation and process optimization.
Supporting evaluation and integration new tools to improve data observability, reliability, and quality control across Kinross’ technology landscape.
Document reusable patterns, standards, and technical guides to drive continuous improvement and maturity in data governance (management, quality and privacy) technical practices.

Education and Experience

Bachelor’s degree in Information Systems, Data Analytics, Computer Science, or a related field;
Master’s degree (or currently pursuing) preferred, or equivalent relevant background.
2-4 years of experience in data management, data quality, or data governance within large or complex enterprise environments.
Solid understanding of data management principles and the key dimensions of data quality, with hands-on experience improving data reliability and consistency.
Experience in data quality and metadata management, including data profiling, rule design/ development and implementation to identify and resolve data quality issues.
Experience developing and maintaining data quality frameworks, metrics, and dashboards (Power BI preferred) to measure and track the health and integrity of critical datasets.
Knowledge of data masking and scrambling techniques, including privacy-preserving and deterministic methods for protecting sensitive data.
Proficiency in SQL for data profiling and data load validation, with familiarity in APIs and automation frameworks for data quality monitoring and process automation.
Experience with metadata ingestion and extraction using data catalog capabilities, including technical lineage building and metadata-driven process improvement.
Experience leading or contributing to Data Management and Quality Enablement initiatives, including presenting in workshops, hosting demos, and facilitating roundtables to promote adoption of data management and data quality practices.

Other

Proficiency with data cataloging and metadata management tools such as Alation (preferred), Informatica, Collibra, or similar, including use of profiling and data quality flagging for monitoring and validation.
Experience developing data quality and masking frameworks in cloud or hybrid environments (Databricks preferred).
Familiarity with data strategy, architecture, modeling, and UX/UI concepts.
Knowledge of data governance, management, and privacy frameworks (DAMA, PII/GDPR, etc).