Lead and serve as the subject matter expert in the design, development and delivery of data pipelines and value-added data assets across the OU Health data ecosystem
Design, build, and maintain data pipelines that deliver curated, value-added data assets such as data marts and other purpose-built data stores
Ensure data pipelines are optimized, highly reliable, and contain low technical debt
Design, build, and maintain the tools and infrastructure needed to handle large datasets
Enforce data governance policies including data quality, validation, lineage, metadata management, and adherence to healthcare regulations
Develop and implement comprehensive data quality frameworks, addressing issues such as data accuracy, completeness, and consistency
Work closely with different application and operational teams to understand business needs and align data engineering initiatives accordingly
Guide, mentor, quality review and train Data Engineering team and ETS department on technical skills and best practices
Requirements
Bachelor's Degree required
5 or more years in analytics (Business Intelligence, Data Engineering, Data Science, etc.) required
Epic certification/accreditation required within 6 months of hire or within 3 months of class completion
Expert level analytic skills related to working with structured and unstructured datasets
Guide, mentor and train Data Engineering team, Data Scientist and Business Intelligence Developers on technical skills and best practices
Must possess critical thinking and creative problem-solving skills along with the ability to communicate well with stakeholders throughout the organization
Effective communication, project management and organizational skills
Experience supporting and working with cross-functional teams in a dynamic environment
Working knowledge of stream processing and highly scalable data stores
Previous experience manipulating, processing, and extracting value from large, disconnected datasets
Expert level SQL and data manipulation skills
Exposure to big data tools: dbt, SnowPark, Spark, Kafka, etc.
Experience with relational SQL and NoSQL databases, including Snowflake, MS SQL Server, and Postrgres
Experience with integration tools: Fivetran, Matillion, SSIS, dbt, SnowSQL
Exposure to stream-processing systems: IBM Streams, Flume, Storm, Spark-Streaming, etc.
Exposure to consuming and building APIs
Exposure to object-oriented/object function programming languages: Python, Java, C++, Scala, etc.
Experience with statistical data analysis tools: R, SAS, SPSS, etc.
Experience with visual analytics tools: QlikView, Tableau, Power BI etc.
Familiarity to Agile methodology for development
Familiarity with electronic health records and financial systems. i.e., Epic Systems, Workday, Strata etc.
Ability to work independently and within teams
Ability to develop and advise on data asset use to provide solutions to organizational needs