Note: The job is a remote job and is open to candidates in USA. Everforth ECS is seeking a Senior Data Engineer to work remotely. This role will lead the design, development, and optimization of scalable enterprise data pipelines and cloud-native data services supporting the U.S. Consumer Product Safety Commission (CPSC).
Responsibilities
- Lead development of production-grade ETL workflows using Python and Microsoft-based technologies
- Design and optimize scalable ingestion, transformation, and validation pipelines for structured and unstructured datasets
- Implement schema enforcement, data validation, anomaly detection, and quality assurance frameworks
- Architect and manage Azure-based data solutions including Azure Data Lake Storage and Azure SQL
- Design and deploy orchestration workflows using Azure Data Factory and Microsoft Fabric/Foundry
- Develop Python-based data services leveraging libraries such as Pandas, PyTorch, TensorFlow, and related open-source frameworks
- Build APIs and microservices supporting interoperability with analytics and AI/ML platforms
- Implement monitoring, logging, fault tolerance, and performance optimization for large-scale systems
- Collaborate closely with data scientists, analysts, architects, and governance teams to deliver secure, reliable, and analytics-ready datasets
- Support Agile development processes and contribute to continuous improvement initiatives
Skills
- Lead development of production-grade ETL workflows using Python and Microsoft-based technologies
- Design and optimize scalable ingestion, transformation, and validation pipelines for structured and unstructured datasets
- Implement schema enforcement, data validation, anomaly detection, and quality assurance frameworks
- Architect and manage Azure-based data solutions including Azure Data Lake Storage and Azure SQL
- Design and deploy orchestration workflows using Azure Data Factory and Microsoft Fabric/Foundry
- Develop Python-based data services leveraging libraries such as Pandas, PyTorch, TensorFlow, and related open-source frameworks
- Build APIs and microservices supporting interoperability with analytics and AI/ML platforms
- Implement monitoring, logging, fault tolerance, and performance optimization for large-scale systems
- Collaborate closely with data scientists, analysts, architects, and governance teams to deliver secure, reliable, and analytics-ready datasets
- Support Agile development processes and contribute to continuous improvement initiatives
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