Enterprise Data Manager & Architect

WellDyne

About The Position

At our company, we move fast, adapt quickly, and turn challenges into opportunities—all while keeping quality at the heart of everything we do. We believe that work should be a positive and respectful place, and that with the right mindset, anything is achievable. Our Vision: Fulfill the essential promise of pharmacy care and help people to live to their healthiest, happiest, and fullest potential. Our Mission: To be the disruptive force that drives meaningful change within pharmacy services. Summary The Enterprise Data Manager & Architect is responsible for defining, governing, and optimizing how WellDyne identifies, structures, secures, and manages data across transactional, operational, and analytical systems. This role focuses on data ownership, including where data resides, how it is sourced, how it flows across systems, and how it is made available for business consumption. This leader will establish enterprise standards for data architecture, governance, metadata, lineage, access, quality, and stewardship- ensuring WellDyne’s data is accurate, trusted, secured, and usable for operational and analytical needs. Success in this role is measured by clear data sourcing frameworks, mature governance practices, adoption of enterprise data standards, improved data trust, and enabling modern capabilities such as Power BI Copilot, data lakes, and machine learning–enabled insights.

Requirements

Bachelor’s or Master’s in Information Systems, Computer Science, Data Analytics, or related field.
8–12 years of progressive experience in data management, architecture, or governance, including 3–5 years in a lead/architect role.
Experience establishing or maturing a Data Management COE or governance program.
Hands-on experience with data lakes, modern cloud data platforms (AWS/Azure/GCP), SQL, and ETL/ELT.
Experience with Power BI Copilot and enabling natural-language data access.
Deep expertise in data architecture, modeling, metadata, lineage, and governance frameworks (DAMA/DMBOK, DCAM, TOGAF).
Strong understanding of data lakes, semantic modeling, and modern integration patterns.
Ability to define enterprise-wide data structures and ensure adoption.
Excellent communication and influencing skills across technical and business audiences.
Strong leadership, facilitation, and mentorship capabilities.
Ability to simplify complex data concepts for business stakeholders.

Nice To Haves

Exposure to applied machine learning preferred.
Professional certifications preferred (Azure/AWS Data, Power BI, DAMA, TOGAF).

Responsibilities

Define and maintain the enterprise data sourcing strategy, documenting where data exists and how it supports transactional and reporting needs.
Establish and lead the Data Management Center of Excellence, including governance models, standards, and decision frameworks.
Create and enforce enterprise standards for data architecture, modeling, metadata, lineage, quality, MDM, and stewardship.
Ensure compliance with regulatory and data security requirements across systems.
Define enterprise data architecture and canonical data models across transactional, operational, and analytical systems.
Ensure alignment of integrations, data flows, and storage patterns across all platforms (cloud, on-prem, data lakes).
Guide modernization initiatives such as database redesigns, platform consolidation, and legacy system decommissioning.
Partner with cross-functional teams to ensure data is traceable, secure, well-governed, and accessible.
Define strategy and standards for data management platforms including data catalog, lineage, metadata, MDM, quality monitoring, and data lake components.
Oversee the creation and ongoing maintenance of a centralized metadata and report catalog to improve visibility and reduce redundancy.
Lead adoption of scalable cloud-native data platforms and lakehouse architectures.
Enable business teams to self-serve insights through governed data models and Power BI Copilot/NLQ capabilities, without IT owning the reporting function.
Identify practical machine learning applications—such as anomaly detection, forecasting, and natural language insights.
Improve enterprise data literacy and promote standardized data usage across WellDyne.
Mentor BI developers, data engineers, analysts, and other technical contributors.
Serve as an enterprise subject-matter expert and escalation point for data architecture and sourcing decisions.
Collaborate with IT and business stakeholders to ensure consistency and alignment across teams.
Performs additional duties as assigned.