At Moffitt Cancer Center, we strive to be the leader in understanding the complexity of cancer and applying these insights to contribute to the prevention and cure of cancer. Our diverse team of over 9,000 are dedicated to serving our patients and creating a workspace where every individual is recognized and appreciated. For this reason, Moffitt has been recognized on the 2023 Forbes list of America’s Best Large Employers and America’s Best Employers for Women, Computerworld magazine’s list of 100 Best Places to Work in Information Technology, DiversityInc Top Hospitals & Health Systems and continually named one of the Tampa Bay Time’s Top Workplace. Additionally, Moffitt is proud to have earned the prestigious Magnet® designation in recognition of its nursing excellence. Moffitt is a National Cancer Institute-designated Comprehensive Cancer Center based in Florida, and the leading cancer hospital in both Florida and the Southeast. We are a top 10 nationally ranked cancer center by Newsweek and have been nationally ranked by U.S. News & World Report since 1999.
Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer. Join our committed team and help shape the future we envision.

Summary

Senior Data Engineer

Position Highlights:

Senior Data Engineer as a member of the Data Engineering team, will provide expertise in design, deployment and maintenance of data engineering framework essential to support the Enterprise data needs within and external to the organization. This position is responsible for supporting and advancing delivery of data analytics projects by designing, building and managing data ingestion, transformation and maintenance to deliver accurate and reliable data to stakeholders in cloud-based and on-premise environment. The Senior Data Engineer is responsible to continuously improve our data engineering operations capability through innovation and developing self-service automations that simplify and improve the overall customer experience. This role combines with technical aspects of data engineering with the business understanding of the systems and processes involved.

The Ideal Candidate will have:

Experience in setting up and managing data lakes & data warehouse for storing and managing Workday data at scale (HCM, SCM, Finance) Experience with Integration of workday data with cloud-based systems (AWS S3, Azure blob storage, snowflake, etc.) for long-term data management.

Responsibilities:

Design, develop, deploy and maintain key deliverables understand business need and develop data models which are efficient, effective and scalable Assist in selecting the right technologies Collaborate with business partners to understand business processes, standard release management Build pipelines in AWS platform (Matillion tool) and Informatica Collaborate with technical, data quality and business teams on design and development activities Update and maintain project, incident and change control systems Build and maintain all required documentation Review daily processes for improvements and enhancements Monitor existing pipelines for errors and perform root cause analysis to develop solutions Act upon opportunities to improve the system process flow, cost, performance, and technical efficiencies Ensure proper application, operations, standards, policies, and procedures are followed. Be a go to person and subject matter expert to the data engineering team Verify code deployments, ensuring the correct build versions have been deployed to new environments. Work with business to define ETL strategy Conduct design sessions to peer review ETL strategy and data models Address production support break-fix in timely manner, keeping IT management and business informed of status and resolutions. Review existing pipelines for errors and perform root cause analysis to develop efficient solutions

Credentials and Experience:

Bachelor’s degree – field of study: IS, Computer Science, Business Minimum 8 years of experience in implementation of Data engineering initiatives Minimum of 3 years of cloud experience (AWS preferred) Minimum of 3 years of experience working and communicating with cross-functional teams, derive requirements and architect data models, shared datasets Expertise in a Data Engineering role, with a focus on building complex data pipelines or conducting data intensive analysis on cloud platform (AWS preference) and on-premise environment Build and maintain the infrastructure to support ETL processing. Extract data from multiple data sources, such as SQL, NoSQL, Oracle and other platform APIs, and load into a centralized data warehouse/ data lake to facilitate unified reporting. Experience with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops Experience working with ETL tools like Informatica, SSIS, Matillion Experience with one or more AWS and/or cloud services: EC2, S3, RDS,Snowflake, Lambda Apply and mentor junior engineers on advanced data engineering theories, principles and concepts Experience working with Spark, Kafka, Airflow A strong engineering background and expertise in working with data and databases Advanced knowledge of T-SQL, SQL