Analytics Engineer

at  Childrens Hospital of Philadelphia

MINIMUM REQUIREMENTS:

Requires a Bachelor’s degree in Computer Science, Analytics or any applicable field and 3 years of related experience, or alternatively a Master’s degree in same and 1 year of related experience. Experience must include working with the EPIC Electronic Health Record (EHR) system, Apache Airflow, IBM Netezza, Qlik, dbt (data build tool), Jenkins, Jira, and Python, R and SQL programming languages.

Responsible for the entire workflow of data associated within the domain, including data pipeline development, ETL performance, timely loading of data sets, and data maintenance. Model raw data sets into curated, reusable, trusted data sets which power analytics across the enterprise and maintain data quality within these data sets via the use of monitoring, testing, and automation. Build automated, scalable, test-driven ETL pipelines. Work with various business units, data analysts, and data scientists to obtain a deep understanding of operational data and develop scalable data products which empower data-driven decision making across the enterprise. Codify reusable data access patterns to speed up development time. Collaborate with business subject matter experts, data analysts, and data scientists to develop well-defined, integrated, re-usable data sets which power analytics. Perform Logical and Physical data modeling using Agile methodology. Implement data quality frameworks and data quality checks. Utilize software development practices such as version control via Git, CI/CD, and release management. Work with Data Scientists, Statisticians and Machine Learning engineers to implement/scale advanced algorithms to solve health care, operational and quality challenges. Make recommendations about platform adoption, including technology integrations, application servers, libraries, and frameworks. Help define the analytical product roadmap to drive the business goals and superior quality outcomes. Improve the effectiveness of data analysts by providing technical expertise in query development, extending data models via the addition of new metrics, and consulting on software development practices. Build data products such as dashboards and reports using various visualization, Business Intelligence tools, and data science tools.