SENIOR DATA ENGINEER

Senior Data EngineerWorking at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer. As the only National Cancer Institute-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times' Top Workplaces.Position Highlights:The Senior Data Engineer, as a member of the Data Engineering team, will provide expertise in the design, deployment, and maintenance of data engineering frameworks that support Enterprise data needs within and external to the organization. This position advances data analytics initiatives by delivering accurate, reliable, and AI-ready data across cloud-based and on-premise environments. The Senior Data Engineer will continuously improve data engineering capabilities through innovation, modern data technologies, and self-service automation that simplifies and enhances the customer experience. This role combines technical expertise in data engineering with an understanding of business systems and processes to enable trusted data for analytics, AI, and emerging data-driven capabilities.Responsibilities:Perform engineering tasks including data analysis, data modeling, reverse engineering, and performance optimizationDesign, develop, and deploy data pipelines and solutions supporting data warehouses, marts, and lakehouse environmentsBuild and maintain infrastructure for efficient extraction, transformation, and loading of data from various sources using AWS, Azure, Snowflake, SQL, and modern lakehouse architectures leveraging Parquet and Apache IcebergDocument technical specifications, data models, process flows, standards, processes, and proceduresDefine and implement data engineering strategies and process improvements to optimize performance, scalability, reliability, data quality, security, cost, data delivery, and automationEstablish and implement practices for data quality, testing, monitoring, error handling, and deployment of data solutionsGuide and mentor data engineering team members on advanced data engineering concepts, technologies, and best practicesProvide rotational on-call production support, including monitoring and facilitating resolution of issues affecting data engineering servicesEnsure design, testing, documentation, and implementation practices comply with HIPAA and other patient data management policies and requirementsMinimum Requirements:Bachelor's degree in information systems, Computer Science, Business Administration, or a related fieldMinimum of 8 years of experience implementing data engineering initiativesMinimum of 3 years of cloud experience with AWS and/or AzureMinimum of 3 years of experience working and communicating with cross-functional teams, including deriving requirements and architecting data models, shared datasets, and AI-ready data solutionsExperience:Experience with modern and traditional data integration and transformation technologies, including dbt, AWS Glue, Informatica, Azure Data Factory, and cloud-native data engineering frameworks.Hands-on experience designing and implementing Snowflake solutions, including data ingestion, ELT, data modeling, Streams, Tasks, Dynamic Tables, Snowpipe/Snowpipe Streaming, and Snowpark, with a focus on performance, security, workload management, and cost optimization.Strong experience with dbt, including models, incremental models, snapshots, tests, macros, packages, documentation, and reusable transformation frameworks, integrated with Git, CI/CD, and DevOps practices for automated testing, code reviews, and deployment across development, test, and production environmentsExperience integrating dbt with Git, CI/CD pipelines, and DevOps practices to support automated testing, code reviews, deployment, and promotion across development, test, and production environments.Experience with Azure data services, including Azure Data Factory, Azure Data Lake Storage (ADLS Gen2), and Microsoft Fabric, for data ingestion, orchestration, transformation, and data platform developmentExperience designing and building cloud-based data lake and lakehouse architectures using AWS, Azure, Parquet, Apache Iceberg, and related cloud servicesExperience designing and building AI-ready data solutions, including curated and governed data products, metadata, data quality, and data structures that support analytics, machine learning, generative AI, and AI/ML workloadsExperience working with Spark, Kafka, and Airflow for distributed processing, streaming, and workflow orchestrationStrong engineering background with expertise in data and database technologies, including advanced T-SQL and SQLExperience working on Unix platforms and using scripting and programming languages such as PythonExperience working in Agile/SCRUM and software development environments, applying software engineering principles, code management, testing, and production support practices; ability to apply advanced data engineering principles and mentor team membersContinuously seek and apply industry best practices and emerging technologies to improve data engineering capabilities and develop new solutionsSalary Range$108,492.80 - $165,422.40Salary ranges posted for this position represent the expected base pay range for the role. Actual compensation may vary based on location and a variety of job-related factors, including experience, skills, education, and internal equity among Team Members in similar positions.We are committed to maintaining fair and equitable pay practices and regularly review compensation to ensure alignment across our workforce.If you have the vision, passion, and dedication to contribute to our mission, then we have a place for you!