Principal Data Engineer - Enterprise Data & Analytics

Mayo Clinic • Rochester, MN

4d

About The Position

The Principal Data Engineer serves as a hands-on technical authority responsible for defining and implementing enterprise-scale data architecture and engineering strategies while actively contributing to solution design, development, optimization, and technical delivery. As part of an assigned product team, this role develops and deploys data pipelines, integrations, and transformations to support analytics and machine learning applications using open-source programming languages and vendor software. The position requires a strong understanding of the organization’s current solutions, coding languages, tools, and Enterprise Data and Analytics technology framework, as well as the ability to apply independent judgment, provide consultative services to departments, divisions, and leadership committees, and partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, transform data, visualize trends, build and validate analytical models, and translate qualitative and quantitative assessments into actionable insights. Key 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.
• Ability to stay current on healthcare trends and enterprise changes.
• Interpersonal skills.
• Time management skills.
• Demonstrated experience working on cross functional teams.
• Strong analytical skills and the ability to identify and recommend solutions.
• Commitment to customer service.
• Excellent verbal and written communication skills.
• Attention to detail.
• High capacity for learning and problem resolution.
• Advanced experience in SQL.
• Advanced Experience in scripting languages such as Python, JavaScript, PHP, C++ or Java & API integration.
• Experience in hybrid data processing methods (batch and streaming) such as Apache Spark, Hive, Pig, Kafka.
• Experience with big data, statistics, and machine learning.
• Ability to navigate Linux and Windows operating systems.
• Knowledge of workflow scheduling (Apache Airflow Google Composer).
• Knowledge of Infrastructure as code (Kubernetes, Docker).
• Knowledge of CI/CD (Jenkins, Github Actions).
• Experience in DataOps/DevOps and agile methodologies.

Nice To Haves

• Experience in hybrid data virtualization such as Denodo.
• Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google Big Query.
• 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.

Responsibilities

• Define and implement enterprise-scale data architecture and engineering strategies.
• Contribute to solution design, development, optimization, and technical delivery.
• Develop and deploy data pipelines, integrations, and transformations to support analytics and machine learning applications.
• Apply independent judgment and provide consultative services to departments, divisions, and leadership committees.
• Partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, transform data, visualize trends, build and validate analytical models, and translate assessments into actionable insights.
• Actively design, develop, review, and optimize production code and platform capabilities.
• Provide technical leadership and mentorship to engineering teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Associate degree