Senior Data Engineer - API Platform Engineer - CDH

Mayo Clinic•Rochester, MN

About The Position

The Senior Data Engineer - API & AI Gateway Platform Engineer designs, develops, and supports enterprise API and AI gateway platforms using technologies such as Apigee (Edge, X, Hybrid), Palo Alto Prisma AIRS (Portkey), Akamai API Security, and related integration frameworks. The role combines API and AI gateway architecture, integration, platform engineering, and data enablement. It operates with a high degree of autonomy and includes leading architectural decisions, influencing platform strategy, and mentoring engineers in platform best practices. The engineer partners with application, architecture, and platform teams to deliver consistent API and AI gateway capabilities, governance, and operational excellence. The position is part of the enterprise Information Security Modernization (ISM) program and supports secure, scalable API, AI gateway, and API security platform capabilities. Participation in a rotating on-call support schedule is required. The position requires a relevant bachelor's degree and at least five years of professional or research experience in data visualization, data engineering, or analytical modeling techniques, or a relevant associate's degree and at least seven years of such experience. In-depth business or practice knowledge may also be considered. Advanced SQL, scripting and API integration, hybrid batch and streaming data processing, big data, statistics, machine learning, and experience using Linux and Windows are required. The role also requires the ability to manage multiple priorities, work cross-functionally, communicate effectively, analyze problems, and recommend solutions. The vacancy is not eligible for visa sponsorship or transfer, and Mayo Clinic does not participate in the F-1 STEM OPT extension program.

Requirements

A relevant bachelor's degree in engineering, mathematics, computer science, information technology, health science, or another analytical or quantitative field, plus at least five years of professional or research experience in data visualization, data engineering, or analytical modeling techniques; or a relevant associate's degree plus at least seven years of such experience. In-depth business or practice knowledge may also be considered.
Advanced experience in SQL.
Strong experience in scripting languages such as Python, JavaScript, PHP, C++, or Java, and API integration.
Experience with hybrid batch and streaming data processing methods such as Apache Spark, Hive, Pig, or Kafka.
Experience with big data, statistics, and machine learning.
Ability to navigate Linux and Windows operating systems.
Ability to manage a varied workload with multiple priorities and stay current on healthcare trends and enterprise changes.
Interpersonal and time-management skills, with demonstrated experience working on cross-functional teams.
Strong analytical skills and the ability to identify and recommend solutions, with a commitment to customer service.
Excellent verbal and written communication, attention to detail, and a high capacity for learning and problem resolution.

Nice To Haves

Knowledge of workflow scheduling, such as Apache Airflow or Google Composer; infrastructure as code and container technologies, such as Kubernetes and Docker; and CI/CD tools such as Jenkins or GitHub Actions.
Experience in DataOps/DevOps and agile methodologies.
Experience with data virtualization such as Denodo.
Working knowledge of Tableau, Power BI, SAS, ThoughtSpot, DASH, d3, React, Snowflake, SSIS, and Google BigQuery.
Google Cloud Platform certification.
Experience designing, deploying, or operating API management platforms or gateways, including API proxy development, policy enforcement, and integration patterns; Apigee X experience is preferred.
Experience with cloud-native infrastructure and container platforms, including Google Cloud Platform, Kubernetes, and Docker, and building and supporting scalable, highly available distributed systems.
Experience with Infrastructure as Code tools such as Terraform or Helm and CI/CD pipeline development using Azure DevOps, Jenkins, or GitHub Actions.
Experience with networking in cloud and hybrid environments, including DNS, load balancing, ingress/egress patterns, firewall rules, and private connectivity.
Experience deploying and managing multi-cloud or hybrid environments, including integration between on-premises and cloud platforms.
Kubernetes certification.

Responsibilities

Lead design and architecture of enterprise API and AI gateway platform capabilities aligned with enterprise standards, scalability, and long-term maintainability.
Build and manage scalable API proxies and services for REST and SOAP integrations.
Implement API security frameworks, including OAuth, JWT, API key management, mTLS, and secure communication protocols.
Implement and maintain enterprise AI gateway services.
Develop and enforce API governance standards, including versioning, lifecycle management, and API design best practices.
Support API and AI gateway deployments across GCP, Azure, on-premises, and hybrid environments.
Develop CI/CD pipelines and automation frameworks for consistent API and AI gateway deployment and lifecycle management.
Configure and manage API traffic policies, including rate limiting, caching, throttling, and load balancing.
Monitor and analyze API performance using analytics, logging, and observability tools; identify and resolve performance and reliability issues.
Integrate APIs with backend systems, microservices, and enterprise platforms to enable data exchange.
Participate in migrations and modernization, including transitions from legacy API platforms to cloud-native or hybrid API solutions.
Ensure compliance with enterprise security, regulatory, and data privacy requirements, including secure logging and data masking.
Collaborate with developers, architects, and platform teams to translate business and technical requirements into scalable API solutions.
Provide operational support, including troubleshooting, incident response, and continuous improvement of API platform capabilities.
Develop and maintain platform documentation, runbooks, and onboarding materials for internal users and developers.
Support proofs of concept and innovation initiatives to evaluate API and integration technologies.
Participate in a rotating on-call support schedule.