Senior Data Engineer handling data engineering responsibilities in hybrid setting for banking industry. Collaborating with cross - functional teams and maintaining data quality in Azure environments.
About the role
- Junior Data Engineer collaborating with healthcare data sources and cross-functional teams to enhance data service capabilities. Developing and maintaining enterprise data management solutions for reporting and analysis.
Responsibilities
- The Junior Data Engineer will work as part of the Data Management team to derive business value from enterprise data by implementing technical specifications provided by the Data Architect and Director of Data Operations & Engineering, including data storage, processing, transformation, ingestion, consumption, and automation.
- This role will work with multiple healthcare data sources and cross-functional teams to establish integrated datasets across legacy and greenfield data systems/platforms.
- Develop, implement, and maintain enterprise data management solutions to enable organizational business intelligence, reporting, visualization, and analysis.
- Assist with the development, implementation and maintenance of an overall organizational data strategy that is in line with business processes.
- Design and build data processing flows to extract data from various sources, such as databases, API endpoints, and flat files.
- Load data into data storage systems, specifically Microsoft SQL Server, MongoDB, and Snowflake.
- Transform data using industry-standard techniques such as standardization, normalization, de-duplication, filtering, projection, and aggregation.
- Build and maintain data processing environments, including hardware and software infrastructure.
- Collaborate with data producers, consumers, and subject matter experts to ensure smooth dissemination and flow of data within the organization.
Requirements
- Minimum of 2+ years of experience working in data-related positions with increasing responsibility and scope of duties
- 2+ years working with relational databases
- 1+ years working with analytical data workloads
- 1+ years working with batch data processing technologies
- Bachelor's Degree, or commensurate directly related work experience, is required with a concentration in a data-related field such as Computer Science, Informatics, Mathematics, Engineering, etc.
- Demonstrated experience with relational and non-relational data storage models, schemas, and structures used in data lakes and warehouses for big data, business intelligence, reporting, visualization, and analytics
- Hands-on experience with extract, transform, load (ETL) process design, data lifecycle management, metadata management, and data visualization/report generation
- Practical experience with industry-accepted standards, best practices, and principles for implementing a well-designed enterprise data architecture.
- Required Languages: Python and SQL
- Required Libraries: PyData stack, Dask, and Prefect
- Knowledge of healthcare interoperability standards such as HL7 (Health Level 7), FHIR (Fast Healthcare Interoperability Resources), CDA (Clinical Document Architecture), etc.
- Knowledge of healthcare clinical code sets such as LOINC, SNOMED, CPT, ICD-10, etc.
- Working knowledge of data flow orchestration tools such as Prefect and Airflow is preferred.
Benefits
- Contexture provides a comprehensive benefits package. For details, please request a Benefit Summary from our Benefits Department.
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