Data Governance & Analytics Lead

Wright-Patt Credit Union Inc.; Beavercreek (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Wright-Patt Credit Union Inc. is seeking a Data Governance Lead to establish and operationalize governance for a modern data and analytics platform, ensuring trusted data across the organization to support strategic initiatives and a data-driven culture.

You will work with Quantitative, BI and Data Science teams, partner with Strategy and IT, define metadata, access controls, data quality rules and enterprise data models to enable secure, scalable self-service analytics.

Qualifications

• Experience leading data governance programs for enterprise data platforms.
• Ability to collaborate with data owners, stewards, IT, and business units.
• Strong understanding of data quality, metadata, and modeling for analytics.

Responsibilities

• Act as liaison for Data Governance Council translating policy into processes and configurations.
• Define, maintain data dictionaries, glossaries, lineage, and metadata catalogs.
• Manage access controls, row-level security, masking, and tagging to meet privacy standards.
• Collaborate with owners to establish data ownership and promote data literacy.
• Define and monitor data governance KPIs and report to governance council.
• Design and deploy data quality profiling and remediation workflows.
• Design and build enterprise data models on the cloud analytics platform.
• Provide cross-functional enablement and training on data governance practices.

Skills

Data governance

Stakeholder collaboration

Cloud data platforms

Metadata management

Tools

Data catalog tools

Job description

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.

Data Governance Implementation & Operationalization (40%):

• 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.
• Define, build, and maintain enterprise data dictionaries, business glossaries, data lineage maps, and metadata catalogs to ensure data accessibility, transparency and consistent KPI definitions.
• 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.
• Collaborate with business, data owners and data stewards to establish data ownership, clarify definitions, and promote a culture of data literacy and accountability.
• Define, track and monitor data governance KPIs and measurement plans to report to the Data Governance Council to track performance and outcomes.

Data Quality Analysis & Remediation (25%):

• Design, deploy, automate and monitor data quality profiling and measurement frameworks to continuously evaluate completeness, accuracy, consistency, and validity.
• Proactively identify anomalies, systemic bugs, and integrity gaps in data and identify root causes.
• Partner with source-system owners, business experts, and data stewards to establish systemic validation rules, exception handling workflows, and automated remediation solutions.
• Develop and maintain comprehensive data quality scorecards and dashboards to report health metrics regularly and provide transparency around data quality.

Data Curation and Modeling (25%):

• Collaborate with business experts, analysts, BI developers and data scientists to gather requirements and input required for designing new or enhancing existing data models.
• 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.).
• 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.
• Create and continuously maintain data model diagrams, data mapping, lineage and transformation documentation that can be shared for transparency and proper usage.

Cross-Functional Support & Enablement (10%):

• Provide content for communication and awareness when new processes, standards, policies, tools and capabilities are implemented.
• Establish data literacy and adoption program to ensure stakeholders can be trained on policies, standards, capabilities as well as data, metadata and tool availability.
• Conduct training and knowledge sharing sessions with stakeholders across the organization to keep them informed on the latest developments.
• Respond to questions or issues that arise around data governance, data quality and enterprise data models within the cloud data and analytics platform.
• 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.