Flynn is seeking a Data Lead to establish a modern enterprise data practice — an effort running in parallel to our ERP program and foundational to its success. Sitting within IT and reporting to the Director, Applications, this newly created role will define how enterprise data is governed, structured, and trusted across Flynn.
Working with stakeholders, the Data Lead will design the enterprise data operating model — governance frameworks, ownership structures, data quality controls, and risk oversight — and ensure enterprise integration platforms operate within well-defined guardrails that support high-quality, well-owned data across systems and regions.
As the first mover in Flynn’s broader transformation, this role will play a key part in the ERP program while building the data foundation the rest of the ecosystem will be built upon. It is not about maintaining an existing program; it is about creating one.
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.
Experience
7–10 years’ experience in enterprise data governance, master data management, data quality, or data leadership roles.
Proven experience establishing or formalizing enterprise data operating models, stewardship structures, or governance frameworks.
Experience working with or alongside enterprise integration platforms, including Workato, Boomi, or similar iPaaS technologies.
Experience working with or alongside enterprise integration platforms (e.g., Boomi, Workato, MuleSoft, or similar iPaaS technologies).
Strong understanding of how integration platforms support master data management, data quality, and cross‑system data consistency.
Experience operating in complex, multi‑system enterprise environments; experience in construction or industrial organizations is an asset.
Skills & Knowledge
Strong understanding of enterprise data governance frameworks, stewardship models, and data risk management.
Deep knowledge of MDM, metadata, data lineage, and enterprise data quality management.
Ability to influence senior stakeholders and drive adoption of structured data practices across IT and the business.
Strong communication skills with the ability to translate complex data concepts into practical, actionable guidance.
Proficiency in data profiling, cleansing, and analysis techniques using SQL, Excel, Python, R, and BI tools.
Education & Certifications
Bachelor’s degree in Information Systems, Computer Science, Data, or a related discipline.
Data governance or data management certifications (e.g. CDMP, CDGP, DGSP) considered an asset.
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