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The Senior Data Engineer - AI Program develops and deploys data pipelines, integrations and transformations to support analytics and machine learning applications and solutions as part of an assigned product team using various open-source programming languages and vended software to meet the desired design functionality for products and programs. The position requires maintaining an understanding of the organization's current solutions, coding languages, tools, and regularly requires the application of independent judgment. Will provide consultative services to departments/divisions and leadership committees. Demonstrated experience designing, building, and operating large-scale healthcare data platforms and data ecosystems, including the movement, transformation, and optimization of structured and unstructured clinical, operational, and research data across on-premises and cloud environments. Candidate will partner with product owners, clinical stakeholders and AI/ML experts to identify and retrieve data, conduct exploratory analysis, pipeline and transform data to support the creation of agentic systems and the build of state-of-the-art multi-modal foundation models. Candidate will provide technical leadership in architecting scalable, cost-efficient data solutions, optimizing data movement and storage strategies, and ensuring secure, compliant access to healthcare data assets across hybrid and multi-cloud environments.
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 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. 

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. 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. 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 required. Experience in DataOps/DevOps and agile methodologies is required. 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. 
Preferred qualifications:
An advanced degree is preferred. 
Strong healthcare data knowledge including electronic health records (EHR), clinical, operational, imaging, genomic, and research data domains, as well as familiarity with healthcare interoperability standards such as HL7, FHIR, DICOM, OMOP, and related healthcare data models.

Demonstrated experience designing and optimizing large-scale data movement, integration, and transformation solutions involving terabyte- to petabyte-scale datasets, with consideration for performance, scalability, reliability, and cost efficiency.

Experience architecting and supporting hybrid data platforms spanning cloud and on-premises environments, including data residency, security, governance, and compliance requirements.

Experience with multiple cloud platforms such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure, including cloud-native data engineering services and cross-cloud data integration patterns.

Experience evaluating and optimizing data transfer, storage, and compute costs while meeting performance, availability, and service-level objectives.

Knowledge of healthcare data governance, data quality frameworks, master data management, metadata management, and regulatory requirements including HIPAA and related healthcare privacy standards.

Experience supporting AI/ML, generative AI, and foundation model initiatives through the development of scalable, high-quality data pipelines and data products.

Demonstrated ability to provide technical leadership and architectural guidance for enterprise-scale data engineering initiatives.