Principal Data Engineer - Enterprise Data & Analytics - Remote

Mayo Clinic is a top-ranked healthcare provider dedicated to patient care and employee well-being. They are seeking a Principal Data Engineer to define and implement enterprise-scale data architecture and engineering strategies, contributing to solution design, development, and optimization for analytics and machine learning applications.

Responsibilities:

• These positions are hands-on engineering roles. In this role, employees are expected to actively design, develop, review, and optimize production code and platform capabilities while providing technical leadership and mentorship to engineering teams

Requirements:

• A Bachelor's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of seven years of professional or research experience in data visualization, data engineering, analytical modeling techniques; OR an Associate's degree in a relevant field such as engineering, mathematics, computer science, information technology, health science, or other analytical/quantitative field and a minimum of nine years of professional or research experience in data visualization, data engineering, analytical modeling techniques
• In-depth business or practice knowledge will also be considered
• Ability to manage a varied workload of projects with multiple priorities and stay current on healthcare trends and enterprise changes
• Interpersonal skills, time management skills, and demonstrated experience working on cross functional teams are required
• Strong analytical skills and the ability to identify and recommend solutions and a commitment to customer service
• Excellent verbal and written communication skills, attention to detail, and a high capacity for learning and problem resolution
• Advanced experience in SQL is required
• Advanced Experience in scripting languages such as Python, JavaScript, PHP, C++ or Java & API integration is required
• Experience in hybrid data processing methods (batch and streaming) such as Apache Spark, Hive, Pig, Kafka is required
• Experience with big data, statistics, and machine learning is required
• Ability to navigate linux and windows operating systems is required
• Knowledge of workflow scheduling (Apache Airflow Google Composer), Infrastructure as code (Kubernetes, Docker) CI/CD (Jenkins, Github Actions) is required
• Experience in DataOps/DevOps and agile methodologies is required
• Expert-level proficiency in Python and SQL with extensive experience developing enterprise-scale production systems
• Advanced expertise in scalable distributed computing frameworks and modern data processing platforms
• Advanced experience implementing and governing open data architectures utilizing Apache Iceberg, Delta Lake, Apache Hudi, and related technologies
• Deep understanding of modern analytical storage formats including Parquet, Avro, and ORC
• Demonstrated expertise in lakehouse architecture, data platform design, and large-scale data engineering practices
• Experience architecting and implementing cloud-agnostic solutions across multiple technology ecosystems
• Experience designing highly scalable, fault-tolerant, secure, and observable data platforms supporting analytics, AI, machine learning, and operational workloads
• Experience establishing enterprise engineering standards, architecture patterns, and modernization strategies