Senior Data Engineer

Build HIPAA-aligned, AI-ready data capabilities that help advance analytics at Moffitt Cancer Center. This Senior Data Engineer role focuses on designing and operating modern data engineering frameworks across cloud-based and on-premise environments, with reliable data pipelines, lakehouse infrastructure, and strong quality and monitoring practices. You will also take part in rotational on-call production support to keep data engineering services running smoothly.

Responsibilities

• Perform engineering work including data analysis, data modeling, reverse engineering, and performance optimization.
• Design, develop, and deploy data pipelines and solutions for data warehouses, marts, and lakehouse environments.
• Build and maintain extraction, transformation, and loading (ETL/ELT) infrastructure using AWS, Azure, Snowflake, SQL, and modern lakehouse architectures such as Parquet and Apache Iceberg.
• Document technical specifications, data models, process flows, and standards, including procedures used by the team.
• Define data engineering strategies and process improvements to optimize performance, scalability, reliability, data quality, security, cost, and automation.
• Establish practices for data quality, testing, monitoring, error handling, and safe deployment of data solutions.
• Guide and mentor data engineering team members on advanced concepts, technologies, and best practices.
• Provide rotational on-call production support, including monitoring and facilitating resolution of issues affecting data engineering services.
• Ensure design, testing, documentation, and implementation practices comply with HIPAA and other patient data management policies and requirements.

Requirements

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

Salary

$108,492.80 - $165,422.40 per year (expected base pay range). Actual compensation may vary based on location and other job-related factors, including experience, skills, education, and internal equity. Moffitt Cancer Center reviews compensation regularly to support fair and equitable pay practices.

Key Technologies

AWS, Azure, Snowflake, SQL, Parquet, Apache Iceberg, AWS Glue, Informatica, Azure Data Factory, dbt, Git, CI/CD, DevOps, Snowpipe, Snowpipe Streaming, Snowpark, Snowflake Streams, Snowflake Tasks, Snowflake Dynamic Tables, Azure Data Lake Storage (ADLS Gen2), Microsoft Fabric, Spark, Kafka, Airflow, T-SQL, Unix platforms, Python, Agile/SCRUM.

Experience

• Experience with modern and traditional data integration and transformation tools, including dbt, AWS Glue, Informatica, Azure Data Factory, and cloud-native data engineering frameworks.
• Hands-on design and implementation of Snowflake solutions, including ingestion, ELT, modeling, Streams, Tasks, Dynamic Tables, Snowpipe/Snowpipe Streaming, and Snowpark, with focus on performance, security, workload management, and cost optimization.
• Strong dbt experience with models, incremental models, snapshots, tests, macros, packages, and documentation, including reusable transformation frameworks integrated with Git, CI/CD, and DevOps practices.
• Experience integrating dbt with Git, CI/CD pipelines, and DevOps to enable automated testing, code reviews, and promotion across development, test, and production environments.
• Experience with Azure data services, including Azure Data Factory, ADLS Gen2, and Microsoft Fabric for ingestion, orchestration, transformation, and platform development.
• Experience building cloud-based data lake and lakehouse architectures using AWS, Azure, Parquet, Apache Iceberg, and related services.
• Experience designing AI-ready data solutions, including curated and governed data products, metadata, and data structures that support analytics, machine learning, generative AI, and AI/ML workloads.
• Experience with Spark, Kafka, and Airflow for distributed processing, streaming, and orchestration.
• Strong engineering background with advanced T-SQL and SQL.
• Experience working on Unix platforms and using scripting and programming languages such as Python.
• Experience in Agile/SCRUM environments and software development practices, including code management, testing, and production support, along with ability to apply advanced principles and mentor team members.
• Ongoing effort to apply industry best practices and emerging technologies to improve data engineering capabilities and develop new solutions.