Director of Product, Oasis

ELLKAY, LLC•Elmwood Park, NJ

20d•Hybrid

About The Position

This role leads the strategy and execution of ELLKAY’s Oasis (Healthcare Data Archive) products. The focus is on helping healthcare organizations modernize safely by decommissioning legacy systems while retaining secure, reliable access to historical clinical, financial, and operational data. This role positions the Archive as a trusted foundation for interoperability, analytics, and future artificial intelligence use cases, supporting continuity of care, compliance, and operational efficiency.

Requirements

Health IT product management.
Archive and data platform design, including search, access control, auditing, and compliance.
Cloud-based delivery models, observability, and data pipelines.
Collaboration on launches, pricing, and packaging.
EMR and other clinical data structures and underlying technologies.
7-10 years of experience in product management
Healthcare data workflows such as record access, compliance retrieval, and legacy system retirement.
Leading cross-functional teams across engineering, delivery, support, sales, and marketing.
Large-scale data migration or conversion programs.
Healthcare technology and software-as-a-service platforms.
Modern data automation and artificial intelligence techniques applied to search and data quality.
Real world AI experience including model design, RAG tuning, and operationalizing AI products.

Nice To Haves

Prior product leadership experience within a health system, payer, or healthcare technology vendor.
A track record of delivering products that produce measurable operational and financial results.
A clear point of view on where healthcare data management is heading and how to win.
Ability to translate regulatory and risk requirements into practical product decisions.
Comfort using strategic frameworks to differentiate products in competitive markets.
Advanced degrees in healthcare or business are a plus, but not required.

Responsibilities

Drive scale, automation, adoption across our Archive suite.
Expand the Archive into broader use cases and customer segments, including cloud-native capabilities and AI applications.
Position the Archive as the durable data foundation for AI and the core platform from which ELLKAY will develop and operate a range of data assets.
Define product strategy for legacy system decommissioning, including data extraction, migration, translation, and long-term archival access.
Own requirements for extracting data from legacy electronic medical record systems such as Epic, Oracle Health, and MEDITECH, including both structured and unstructured content.
Guide development of migration tooling that supports discrete data delivery for import into new electronic health record systems, while also supporting full historical archiving.
Establish standards for data translation and normalization so legacy data is usable across clinical, financial, reporting, and compliance workflows.
Demonstrate and manage the cost-cutting benefits of operating Oasis for the products install base.
Align with sales and marketing on messaging, launches, and enablement.
Review outcomes regularly and adjust priorities based on results.
Introduce automation to reduce manual activities, including automated analysis of extracted data and notifications.
Establish clear cloud-native product architecture decisions for observability, data pipelines, and artificial intelligence readiness.
Lead packaging, pricing, and implementation playbooks so sales and delivery can execute consistently.
P&L responsibilities including top-line and contribution margin targets.
Scale and simplify delivery by standardizing workflows and configurations, integrating the Archive with connectivity and network capabilities, driving adoption across hospitals, ambulatory practices, laboratories, payers, and partners, and expanding coverage into additional domains including enterprise resource planning, human resources, claims, and specialty areas.
Run the Archive as one unified platform by setting and tracking success measures while strengthening automation and data reuse with data completeness, quality, lineage, and governance that enables enterprise-wide artificial intelligence use cases.