• Work with one of our U.S.-based clients. • Develop and implement data pipelines using PySpark. • Collaborate with cross-functional teams including machine learning engineers, software developers, analytics engineers, and product managers. • Translate business requirements into highly available data solutions. • Maintain and improve existing data pipelines and systems.

• Design, develop, and maintain robust ELT/ETL pipelines • Build and optimize data models in cloud-based data warehouses • Implement and manage orchestration frameworks • Develop streaming and real-time data pipelines • Architect and provision cloud data infrastructure on GCP • Ensure all data systems comply with HIPAA, HITECH, and applicable state privacy regulations • Implement data quality frameworks • Mentor junior data engineers

• Collaboration with U.S-based client stakeholders on solution design/definition. Identify solutions that solve business problems, translating requirements into technical specifications and actionable work. • Writing code and implementing the proposed solutions • Creating data pipelines, versioning and change management • Manage the complexity inherent in versioned data pipelines • Develop ETL/ELT processes to help extract and manipulate data from multiple sources. • Design, build and maintain batch or real-time data pipelines in production. • Automate data workflows such as data ingestion, aggregation, and ETL processing. • Logging and instrumentation of pipelines and services. • Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.

• Collaboration with U.S-based client stakeholders on solution design/definition. Identify solutions that solve business problems, translating requirements into technical specifications and actionable work. • Writing code and implementing the proposed solutions • Creating data pipelines, versioning and change management • Manage the complexity inherent in versioned data pipelines • Develop ETL/ELT processes to help extract and manipulate data from multiple sources. • Design, build and maintain batch or real-time data pipelines in production. • Automate data workflows such as data ingestion, aggregation, and ETL processing. • Logging and instrumentation of pipelines and services. • Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.