Senior Data Engineer: Cloud Pipelines & AI-Ready Data

Moffitt Cancer Center; Florida; Hybrid; USD 108,000 - 165,000; Part time

Job summary

Moffitt Cancer Center is seeking a Senior Data Engineer to design, deploy, and maintain data pipelines across cloud and on-premise environments. You will enable trusted data for analytics, AI, and data-driven capabilities while ensuring HIPAA-compliant data handling.

The role emphasizes building scalable data architectures, mentoring engineers, and delivering accurate, AI-ready data across AWS/Azure ecosystems. Hybrid work options apply in Florida-based operations.

Qualifications

• Bachelor’s degree in information systems, Computer Science, Business Administration, or related field.
• Minimum of 8 years of experience implementing data engineering initiatives.
• Minimum of 3 years of cloud experience with AWS and/or Azure.
• Minimum of 3 years of experience working with cross-functional teams to architect data models and AI-ready data solutions.

Responsibilities

• Design, deploy, and maintain data pipelines and solutions for data warehouses, marts, and lakehouse environments.
• Build and maintain infrastructure for ETL/ELT data extraction from diverse sources using AWS, Azure, Snowflake, SQL, and lakehouse tech.
• Document data models, specifications, processes, and standards; mentor team members in data engineering concepts.

Skills

Cloud data pipelines

Snowflake

dbt

Git

CI/CD

DevOps

Azure Data Factory

Azure Data Lake Storage

Microsoft Fabric

Spark

Kafka

Airflow

Python

Unix

SQL

Education

Bachelor’s degree in Information Systems/CS/related field

Tools

Snowflake

dbt

Git

CI/CD

DevOps

Azure Data Factory

Azure Data Lake Storage

Microsoft Fabric

Apache Iceberg

Job description

Moffitt Cancer Center is seeking a Senior Data Engineer to design, deploy, and maintain data pipelines across cloud and on-premise environments. You will enable trusted data for analytics, AI, and data-driven capabilities while ensuring HIPAA-compliant data handling.

The role emphasizes building scalable data architectures, mentoring engineers, and delivering accurate, AI-ready data across AWS/Azure ecosystems. Hybrid work options apply in Florida-based operations.