Data Warehouse Engineering Manager(REMOTE) #ESF6167

ExpertHiring•Menlo Park, CA

1d•Remote

About The Position

Our client provides full-service health care and personalized support to help you age well at home and in your community. They are a stable company with tremendous opportunity for career advancement where your contributions will be recognized. You will work with an experienced and energetic team and receive a comprehensive, lucrative salary and benefits.

Requirements

Bachelor’s or master’s degree in computer science, information systems, data engineering, or related field.
Minimum of seven (7) years of experience in data engineering or data warehouse architecture, including at least three (3) years in a technical leadership position.
Two (2) years of supervisory experience with demonstrated ability to mentor and develop team members.
Deep expertise in Databricks architecture including Delta Lake, Apache Spark, and MLflow, with strong experience implementing complex data pipelines and transformations.
Hands-on experience architecting and deploying analytics and data infrastructure on Microsoft Azure (Data Factory, Azure SQL, Storage, etc.).
Proven experience implementing DevSecOps frameworks, secure data operations, and governance in regulated industries - healthcare experience strongly preferred.
Strong understanding of CI/CD, infrastructure-as-code, and automation tools such as GitHub Actions, Azure DevOps, or equivalent technologies.
Experience integrating AI and machine learning capabilities to streamline data workflows and enhance analytical insights.

Responsibilities

Lead, mentor, and develop a high-performing team of data engineers, fostering an engineering culture centered on collaboration, innovation, and continuous improvement.
Architect, design, and manage data warehouse and lakehouse solutions on Databricks and Azure, ensuring scalability, security, and compliance with healthcare regulations.
Evaluate and implement AI and machine learning technologies within Databricks and Azure environments to optimize data processes and accelerate analytics capabilities.
Implement DevSecOps principles across the data engineering environment, integrating security, compliance, and automation into every stage of the development lifecycle.
Develop and manage CI/CD pipelines for data engineering workflows to promote automated testing, deployment, and environment consistency.
Drive automation across data ingestion, transformation, and quality frameworks to deliver efficient, robust, and scalable data processes.
Collaborate with Analytics, IT, and business partners to deliver secure, efficient, and compliant data warehouse solutions that meet organizational data needs.
Oversee data modeling, integration, governance, and data quality frameworks that ensure accuracy, consistency, and business trust in analytic data sets.