Job Description

Role: Sr Manager

Technology & Architecture

Demonstrated knowledge of modern architectures supporting healthcare data and AI, including:

• APIs, microservices, and cloud platforms

• Data platforms, interoperability layers, and integration patterns

• Ability to assess and recommend build vs. buy vs. reuse decisions, considering healthcare constraints

• Comfort working with legacy systems, managing technical debt, and handling complex integrations typical of healthcare ecosystems

• Full Stack Engineering

• UI Development: Relevant hands-on experience

• API and Backend Development: Java/J2EE, Spring (Core/Boot/Batch), REST APIs, Microservices, UI Widgets, NextJS

• Data Access: SQL and GraphQL

• Strong analytical and debugging skills using tools such as Splunk Queries, Azure KQL, Grafana, and Dynatrace

• Hands-on experience with:

• Cloud platforms: Azure, Open Shift, AKS

• DevOps tools: GitHub, GitHub Actions, Artifactory, Docker

• Databases: PostgreSQL

• Agile/Scrum methodologies

• Data Engineering

• 15+ years of overall data engineering experience

• 5+ years of experience with cloud technologies, specifically Azure and Snowflake

• 5+ years of experience working in Agile/Scrum environments

• 5+ years of experience leading engineering teams

• Strong SQL expertise

• Data, Analytics & AI Enablement

• Working understanding of data flows across partner systems

• Ability to collaborate closely with data engineering, analytics, and AI teams to:

• Enable reliable healthcare analytics and reporting

• Support decision-making through actionable insights and AI-assisted summarization

• Experience in defining requirements for:

• Operational and clinical dashboards

• KPIs aligned to quality, efficiency, cost, and experience

• AI-enabled analytics, including summarization, prioritization, and decision support

• Strong awareness of data quality, lineage, interoperability, and adoption challenges in healthcare environments

• AI & Automation

• Solid understanding of AI use cases, including:

• Operational summarization

• Human-in-the-loop decision support

• Workflow automation and prioritization

• Ability to balance innovation with:

• Responsible AI practices

• Explainability and auditability

• Regulatory and ethical considerations

• Experience guiding AI initiatives from pilot to production and scaling adoption in healthcare settings

• Delivery & Execution

• Proven experience with Agile and SAFe delivery models, emphasizing progress, readiness, and adoption

• Strong dependency and risk management across:

• Product teams

• Data pipelines

• Analytics and AI platforms

• Expertise in release planning, including:

• Data readiness and validation

• Analytics accuracy

• AI model monitoring and compliance checks

• Stakeholder Management & Communication

• Trusted partner to business, clinical, and operations stakeholders

• Ability to clearly articulate trade-offs, risks, and value realization