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Skills
Analysis Skillsunmatched
Apache Sparkunmatched
Architectural Servicesunmatched
Artificial Intelligence (AI)unmatched
Automationunmatched
Channel Strategiesunmatched
Computer Scienceunmatched
Continuous Deployment/Deliveryunmatched
Continuous Improvementunmatched
Continuous Integrationunmatched
Cross-Functionalunmatched
Data Managementunmatched
Data Modelingunmatched
Data Setsunmatched
Data Warehousingunmatched
DevOpsunmatched
Engineering Managementunmatched
Enterprise Protectionunmatched
GitHubunmatched
Healthcareunmatched
High Level Architecture (HLA)unmatched
Information Technology & Information Systemsunmatched
Information/Data Security (InfoSec)unmatched
Machine Learningunmatched
Mentoringunmatched
Microsoft Infrastructureunmatched
Microsoft Windows Azureunmatched
Operations Security (OPSEC)unmatched
People Managementunmatched
Performance Managementunmatched
Process Improvementunmatched
Product Lifecycleunmatched
Regulatory Complianceunmatched
SQL (Structured Query Language)unmatched
Security Analysisunmatched
Team Buildingunmatched
Team Lead/Managerunmatched
Technical Leadershipunmatched
Test Automationunmatched
Description
Job Summary:
Our client is seeking a Healthcare Enterprise Data Warehousing Security & Analytics Engineering Architect to lead the architecture, engineering, and optimization of a mission-critical data foundation. Built on Databricks and Azure, this platform enables scalable analytics that directly power care delivery for vulnerable seniors.
This is a unique opportunity for a true hands-on technical leader not just a people manager. As the Subject Matter Expert (SME) in Databricks, Azure Data Services, and DevSecOps, you will build a compliant, AI-enabled lakehouse from the ground up. You will blend high-level data architecture and CI/CD rigor with healthcare compliance expertise to scale a modern, secure data platform while mentoring a high-performing, innovation-driven engineering team.
Key Responsibilities:
Team Leadership: Lead, mentor, and develop a high-performing team of data engineers, fostering a culture of collaboration, innovation, and continuous improvement.
Architecture & Design: Architect and manage scalable data warehouse and lakehouse solutions on Databricks and Azure, ensuring maximum security and healthcare regulatory compliance.
AI & Innovation: Evaluate and implement AI/Machine Learning technologies within the environment to optimize data processes and accelerate advanced analytics.
DevSecOps Integration: Implement DevSecOps principles, integrating security, compliance, and automation into every stage of the development lifecycle.
Pipeline Automation: Develop and manage CI/CD pipelines to enable automated testing, deployment, and environment consistency across all data workflows.
Data Governance: Oversee data modeling, integration, and quality frameworks to ensure accuracy, consistency, and organizational trust in analytic data sets.
Cross-Functional Collaboration: Partner with Analytics, IT, and business stakeholders to deliver data solutions that align with clinical and operational needs.
Must-Have Technical Skills:
Enterprise-Scale Databricks: Proven expertise in architecting and implementing Databricks solutions (not just usage), including Delta Lake, Apache Spark, and MLflow.
Production Pipelines: Hands-on experience building complex, production-grade data pipelines using Spark and Delta Lake.
Azure Infrastructure: Deep experience deploying analytics infrastructure on Microsoft Azure (e.g., Data Factory, Azure SQL, Azure Storage, Synapse).
Security & Compliance: Demonstrated experience implementing DevSecOps frameworks and secure data operations within a regulated industry (Healthcare experience strongly preferred).
Automation Tools: Strong proficiency in CI/CD, Infrastructure-as-Code, and automation platforms like GitHub Actions or Azure DevOps.
Professional Experience:
Education: Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related technical field.
Data Engineering Tenure: Progressive experience in data engineering or data warehouse architecture.
Technical Leadership: Prior experience in a technical leadership role (e.g., Lead Engineer, Architect, or Engineering Manager) with hands-on architectural ownership.
Supervisory Experience: Current of direct people management experience, including performance management and team development.