It is an exciting time at Mayo Clinic, as we are building the most trusted generative AI and LLM-based solutions to empower our staff, improve our practice and transform healthcare. To accelerate our generative AI strategy, we are forming a cross functional team of technical experts. This team will be responsible for:

Building the most trusted generative AI and automation solutions to benefit patients worldwide.

Providing temporary tiger-team efforts to accelerate key initiatives.

Expanding the organization’s understanding of LLM technology through:

Development of best practices, knowledge assets, and code examples to accelerate the efforts of others.

Execution of technical proofs of concept and exploration

Providing consultations, presentations, and sharing of knowledge across Mayo Clinic to technical and non-technical audiences

Providing guidance across the Generative AI program workstreams as technical experts.

The Senior Data Engineer will be responsible for developing and deploying data pipelines, integrations, and transformations to support analytics and machine learning applications and solutions as part of an assigned product team. The position requires maintaining an understanding of the organization's current solutions, coding languages, tools, and regularly requires the application of independent judgment. The candidate will partner with product owners and Analytics and Machine Learning delivery teams to identify and retrieve data, conduct exploratory analysis, pipeline, and transform data to help identify and visualize trends, build and validate analytical models, and translate qualitative and quantitative assessments into actionable insights.

This is a full-time remote position within the United States. Mayo Clinic will not sponsor or transfer visas for this position, including F1 OPT STEM.

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 five 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 seven years of professional or research experience in data visualization, data engineering, analytical modeling techniques. In-depth business or practice knowledge will also be considered.

The incumbent must have the 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.

Requires strong analytical skills and the ability to identify and recommend solutions and a commitment to customer service.

The position requires excellent verbal and written communication skills, attention to detail, and a high capacity for learning and problem resolution.

Advanced experience in SQL is required.

Strong 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) that make use of technologies such as Apache Beam, Apache Spark, Kafka, Pub/Sub is preferred.

Experience with big data, statistics, and machine learning is required.

The 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 preferred.

Experience with vector databases and/or managed vector database services such as Google’s Vertex AI Search is preferred.

Experience in DataOps/DevOps and agile methodologies is preferred.

Experience with hybrid data virtualization such as Denodo is preferred.

Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google Big Query is preferred.

Google Cloud Platform (GCP) certification is preferred.