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The Data Governance & Analytics Lead is a strategic leader responsible for establishing and overseeing data governance for a modern data and analytics platform implementation and ensuring trusted data is readily available across the organization to support strategic data and analytics initiatives and foster a data-driven culture. This role will be part of the Data & Analytics team collaborating with Quantitative, BI and Data Science resources while also partnering closely with Strategy, IT (data engineering), and all areas of the business to ensure data can be efficiently and securely leveraged as a strategic asset across the organization to support strategic objectives. This role is responsible for operationalizing and executing the data governance framework based on direct guidance from our Data Governance Council. Concurrently, this role requires strong data and analytics acumen to perform hands-on data profiling, data analysis and lead the design and implementation of trusted enterprise data models and assets within a modern cloud data and analytics platform environment. The Data Governance & Analytics Lead operates with a high degree of autonomy and provides thought leadership in modern enterprise data governance and data management to ensure analytics work is trusted, efficient, actionable, and scalable.

1)      Data Governance Implementation & Operationalization (40%): Implement, operationalize and oversee the enterprise data governance framework based on guidance from the Data Governance Council while providing thought leadership for continuing to enhance and shape data governance processes and standards.

a)       Act as the liaison for the Credit Union's Data Governance Council, translating high-level policy, standards, compliance requirements and decisions into formalized processes, solutions and technical configurations within the cloud data and analytics platform.

b)      Define, build, and maintain enterprise data dictionaries, business glossaries, data lineage maps, and metadata catalogs to ensure data accessibility, transparency and consistent KPI definitions.

c)       Establish and oversee processes for managing user access, row-level security, column-level masking, and object tagging within the cloud data and analytics platform to enforce compliance with financial regulations and internal data privacy standards.

d)      Collaborate with business, data owners and data stewards to establish data ownership, clarify definitions, and promote a culture of data literacy and accountability.

e)      Define, track and monitor data governance KPIs and measurement plans to report to the Data Governance Council to track performance and outcomes.

2)      Data Quality Analysis & Remediation (25%): Ensure processes are implemented, automated and proactively monitor data quality to provide transparency, remediate data quality issues, and build trust in the data across the organization.

a)       Design, deploy, automate and monitor data quality profiling and measurement frameworks to continuously evaluate completeness, accuracy, consistency, and validity.

b)      Proactively identify anomalies, systemic bugs, and integrity gaps in data and identify root causes.

c)       Partner with source-system owners, business experts, and data stewards to establish systemic validation rules, exception handling workflows, and automated remediation solutions.

d)      Develop and maintain comprehensive data quality scorecards and dashboards to report health metrics regularly and provide transparency around data quality.

3)      Data Curation and Modeling (25%): Design, build and maintain comprehensive, reusable, scalable data models and analytical data sets on the cloud data and analytics platform that efficiently and accurately support BI, analytics, and AI needs across the organization.

a)       Collaborate with business experts, analysts, BI developers and data scientists to gather requirements and input required for designing new or enhancing existing data models.

b)      Design and build data models on the cloud data and analytics platform that bring disparate data together and are scalable, performant, and reusable to support broad BI and analytics needs (e.g. enterprise member 360, loan portfolio data mart etc.).

c)       Continuously assess and manage data models that support the BI, analytics, and AI needs to ensure there is a single version of the truth where necessary and models are business ready, defined and catalogued to support efficient and trusted self-service.

d)      Create and continuously maintain data model diagrams, data mapping, lineage and transformation documentation that can be shared for transparency and proper usage.

4)      Cross-Functional Support & Enablement (10%): Partner with business units across the organization to promote enablement, awareness and adoption of standards, policies, tools and processes for leveraging the cloud data and analytics platform.

a)       Provide content for communication and awareness when new processes, standards, policies, tools and capabilities are implemented.

b)      Establish data literacy and adoption program to ensure stakeholders can be trained on policies, standards, capabilities as well as data, metadata and tool availability.

c)       Conduct training and knowledge sharing sessions with stakeholders across the organization to keep them informed on the latest developments.

d)      Respond to questions or issues that arise around data governance, data quality and enterprise data models within the cloud data and analytics platform.

e)      Ensures proper policies, procedures, risk mitigation activities, and operating controls are followed. Reports gaps in policies, procedures, and operating controls to leadership to ensure member impact and risk is mitigated.