Role Description We are hiring a Senior Data Engineer to take a designed-but-not-yet-deployed AWS data platform from architecture to a working MVP at a customer site in Japan and then own its operation from there. - The system has been designed; what's needed is an engineer who can implement it cleanly, get it running in the customer's AWS environment, prove it out at MVP scope, and then run it as it grows. - You'll be the person accountable for the platform from "deployed" through "running reliably in production" through "evolving as the workload grows." - Initial work is focused on standing up infrastructure, implementing data pipelines and backend services, validating the system end-to-end against real operational data, and bringing it live for an MVP deployment. - After launch, the role shifts to operating and extending the platform as the engagement matures. - This role is best suited for engineers who take pride in shipping into production environments where reliability matters and want to own a system over a multi-year arc rather than handing it off. - You will focus on this single engagement rather than being fragmented across multiple projects. Responsibilities - Implement the designed data pipelines and backend services in Python on AWS. - Design and manage AWS infrastructure using Terraform and Terragrunt. - Deploy the platform end-to-end into the customer's AWS environment and bring it live for an MVP launch, validating against real operational data. - Build out the CI/CD, observability, and runbooks needed to operate the platform reliably. - Own the platform after launch — incident response, performance, capacity, and cost. - Lead the platform's design evolution from MVP through later production stages, making the data model, scaling, and reliability decisions informed by running it yourself. Qualifications - Senior-level experience shipping production backend or data services in Python. You have built systems other people depend on. - Production AWS architecture experience, including event-driven services such as SQS, SNS, EventBridge, Step Functions, and Lambda. - Infrastructure-as-code with Terraform (Terragrunt is our standard). You've stood up and managed cloud infrastructure end-to-end. - You've operated systems in production — incident response, performance, capacity, cost — and been the person accountable when something breaks. Preferred Experience - Time-series or high-throughput data environments. - Industrial, manufacturing, robotics, or IoT systems. - ML infrastructure or MLOps tooling. - Large-scale data processing frameworks (e.g., Spark, Databricks). - Japanese language ability is helpful but not required. Location - Remote within the United States. - Must be authorized to work in the U.S. without employer sponsorship. - Collaboration with Japan-based teams is common; Pacific or Mountain time preferred. - Occasional travel to Japan may be available. Benefits - Medical, dental, and vision insurance. - 401(k).

• Develop automations for the data platform using infrastructure as code, ensuring durability, high performance and ease of use while maintaining governance and security; • Maintain the data layer and its services, ensuring they are observable, scalable and flexible to meet the platform’s demands; • Drive engineering team efficiency by developing and implementing frameworks, methods and standards that optimize team activities; • Mentor and develop colleagues in data engineering best practices to promote scalability and cloud automations, collaborating closely with all teams; • Translate business needs into viable, reusable technological solutions for the platform, fostering a data-driven culture; • Create and validate hypotheses, adapting quickly to changes in priorities or situations; • Work autonomously while consistently relying on and supporting the team; • Own the data platform by identifying and refining requirements for improvements, optimizations and technological changes; • Monitor platform availability, performance, capacity and usage patterns, proposing tactical or strategic changes as needed.

Role Description Everforth ECS is seeking a Senior Data Engineer to 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). This role will help modernize and stabilize CPSC’s Azure-based data infrastructure while enabling advanced analytics, machine learning, and Sentinel-driven product safety initiatives. - 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. Qualifications - 5+ years of experience developing and deploying advanced statistical, machine learning, or enterprise data pipeline solutions. - Strong proficiency in Python, including Pandas and related data engineering libraries. - Strong SQL skills and experience integrating relational database systems. - Hands-on experience designing and operating solutions in Azure cloud environments. - Experience developing ETL workflows using Python and Microsoft technologies. - Experience with schema enforcement, data validation, and quality assurance practices. - Experience developing APIs and cloud-native data services. - Familiarity with workflow orchestration tools such as Azure Data Factory. - Experience with performance optimization, logging, and monitoring for enterprise-scale systems. - Familiarity with open-source data processing and ML frameworks such as PyTorch, TensorFlow, NumPy, and scikit-learn. Requirements - Salary Range: $165,000-$180,000 Benefits - General Description of Benefits

Role Description Everforth ECS is seeking a Mid-Level Data Engineer to support the design, development, and optimization of scalable data pipelines and services for the Consumer Product Safety Commission enterprise data management environment. This role will support advanced analytics, machine learning readiness, and modernization of CPSC’s Azure-based data infrastructure. - Develop production-grade ETL workflows using Python and Microsoft-based frameworks. - Ingest, transform, and validate structured and unstructured data. - Implement schema enforcement, data validation, and quality checks. - Support Azure Data Lake Storage, Azure SQL, and Azure-based data services. - Design workflow orchestration using Azure Data Factory or Microsoft Fabric/Foundry. - Build Python-based data services using Pandas, PyTorch, TensorFlow, and related libraries. - Develop API endpoints and microservices to support analytics and ML platform interoperability. - Implement logging, monitoring, performance tuning, and operational reliability. - Collaborate with data scientists, analysts, architects, and governance teams. - Apply data governance best practices for compliance, reproducibility, and auditability. Qualifications - 3+ years of experience developing or supporting advanced statistical, machine learning, or data pipeline solutions. - Proficiency in Python, including Pandas. - Strong SQL skills and experience integrating relational database sources. - Hands-on experience with Azure cloud environments. - Experience with ETL development using Python and Microsoft technologies. - Experience with data validation, schema enforcement, and quality assurance. - Familiarity with open-source data processing libraries such as NumPy, scikit-learn, PyTorch, or TensorFlow. Requirements - This position is contingent upon contract award. - Salary Range: $90,000-$98,000 Benefits - General Description of Benefits