Data Lead

Enterprise Data Governance & Structure 

• Design and implement Flynn’s enterprise data governance framework, including policies, standards, decision rights, and escalation paths. 

• Establish clear enterprise data ownership and stewardship models, defining accountability across key business and master data domains. 

• Develop and maintain core governance artefacts such as enterprise data standards, business glossaries, ownership matrices, and governance workflows. 

• Act as the central authority and escalation point for enterprise data governance decisions and exceptions. 

 Enterprise Master Data Management (MDM) 

• Define the enterprise master data vision and operating model, positioning master data as a foundational business asset at Flynn. 

• Establish golden‑source definitions, lifecycle controls, and approval processes for core enterprise data domains (e.g. customer, supplier, product, employee). 

• Partner with Integration and Application teams to ensure master data is consistently governed and propagated across systems like Workato, Boomi, or equivalent iPaaS platforms. 

Enterprise Data Quality 

• Define enterprise‑wide data quality standards, KPIs, and control points across critical data domains. 

• Design monitoring and reporting mechanisms that provide visibility into enterprise data quality trends and risks. 

• Ensure data quality rules and validation controls are embedded into enterprise data flows and automated integrations tools like Workato, Boomi, or equivalent iPaaS platforms. 

• Work with business and IT teams to address upstream process and integration design issues impacting data quality. 

 Enterprise Integration & Platform Governance 

• Establish governance standards for enterprise data movement, including approved integration patterns, ownership expectations, and quality controls. 

• Provide governance oversight for enterprise integration platforms, ensuring consistent use aligned with enterprise data standards. 

• Influence platform usage and design patterns without owning hands‑on technical configuration. 

Enterprise Data Oversight & Risk 

• Identify, monitor, and mitigate enterprise data risks, including accuracy, security, privacy, and compliance risks. 

• Support the creation and maintenance of enterprise data catalogues, glossaries, and lineage artefacts to improve transparency and trust. 

• Define and report enterprise data governance and quality KPIs to IT and business leadership.