Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related technical field
Progressive experience in data engineering or data warehouse architecture
Prior experience in a technical leadership role (e.g., Lead Engineer, Architect, or Engineering Manager)
Current of direct people management experience, including performance management and team development
Requirements:
Lead, mentor, and develop a high-performing team of data engineers
Architect and manage scalable data warehouse and lakehouse solutions on Databricks and Azure
Evaluate and implement AI/Machine Learning technologies within the environment
Implement DevSecOps principles, integrating security, compliance, and automation into every stage of the development lifecycle
Job 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.
