Data Engineer at Nicklaus Children's Hospital

Miami, Florida, United States -

Full Time

Start Date

Immediate

Expiry Date

02 Oct, 26

Salary

0.0

Posted On

04 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipeline Construction, ETL/ELT, Python, SQL, Azure, AWS, Oracle PL/SQL, SSIS, Informatica, API/RESTful Web Services, XML, JSON, HL7, FHIR, Tableau, Power BI

Industry

Hospitals and Health Care

Description

Job Summary Integrates, builds, and maintains data pipelines and data transformation algorithms for enterprise data analytics consumption. Responsible for data integration, monitoring of data storage, and data distribution across multiple on premise and cloud-based data environments in preparation for sophisticated analytics consumption, data mining, and enterprise data insights. Job Specific Duties Designs and develops highly scalable, end to end process to consume, integrate and analyze large volume, and complex data from dispersed data sources on both on premise and cloud-based data environments and systems. Integrates, builds, and maintains data pipelines and data transformation algorithms for enterprise data analytics consumption. Responsible for data integration, monitoring of data storage, and data distribution across multiple on premise and cloud-based data environments in preparation for sophisticated analytics consumption, data mining, and enterprise data insights. Engages and communicates effectively with a variety of IT, research, and medical staff. Integrates and ETL/ELT datasets and flows using a variety of open source and best in class proprietary software. Write complex, efficient queries against large, and disparate data sources. Monitors ETL/ELT performance and ensures compliance with security. Deploy sophisticated analytics programs, machine learning, and statistical methods. Prepare data for predictive and prescriptive modeling. Find hidden patterns using data to help improve business insights. Use of machine learning patterns for data discovery and on tasks that can be automated. Minimum Job Requirements Bachelor's Degree in IT, Biomedical Informatics, Computer Science, Engineering or related field with at least 4 years of combined experience in Clinical IT and/or Research (or) Master’s degree in IT, Biomedical Informatics, Computer Science, Engineering with at least 2 years of combined experience in Clinical IT and/or Research 1-3 years of data engineering and integration experience 1-3 years of data management in cloud-based data environments such as Azure or AWS 3-5 years of experience developing database schemas, querying, managing, or curating Oracle PL/SQL or SQL server databases 1-3 years of data warehousing ELT/ETL experience 1-3 years of experience with the use of Python, SQL, SSIS, or Informatica for data ETL and data transformations Experience working with API’s/RESTful Web Services Experience with XML, JSON, HL7, or FHIR Experience with data visualization tools such as Tableau or Power BI Knowledge, Skills, and Abilities Direct experience or strong familiarity with commercial EHR platform and/or PeopleSoft. Experience extracting and managing data from large enterprise clinical and non-clinical systems. Familiarity with and experience with efficient database ETL processes – extracting data from a wide range of data sources, normalizing data, creating and providing entity relationship diagrams, schemas and related documentation, creating primary and foreign keys and indexing, writing functional and technical specifications. Ability to learn and apply new technical skills. Willingness and ability to share knowledge with other team members. Ability to adapt to changes in fast paced healthcare and data environment. Identify ways to improve data reliability, efficiency, and quality. Effective work experience developing, integrating, and supporting data systems; working with a combination of both relational and NoSQL databases.

Responsibilities

Design, build, and maintain scalable data pipelines and transformation algorithms for enterprise analytics across on-premise and cloud environments. Monitor data storage and distribution to support data mining, machine learning, and predictive modeling.