• Develop machine learning and AI solutions to solve business and operational challenges • Design, build, validate, and deploy models for forecasting, anomaly detection, customer analytics, capacity planning, and product intelligence • Apply statistical analysis and experimentation techniques to generate actionable insights • Develop dashboards, visualizations, and executive-level reporting to communicate findings and recommendations • Monitor model performance and support continuous improvement initiatives • Partner with business stakeholders to define key metrics, KPIs, and success measures across products and operations • Design scalable enterprise data architectures supporting structured, semi-structured, and unstructured data workloads • Define data models, metadata standards, governance frameworks, and architectural best practices • Architect modern data platforms leveraging cloud, hybrid-cloud, lakehouse, and distributed data technologies • Establish data integration strategies across CRM, ERP, product usage, support, operational, and business systems • Build scalable ETL/ELT pipelines and data services that support analytics and AI workloads • Drive adoption of data quality, lineage, security, privacy, and compliance standards • Partner with product, engineering, and business leaders to identify high-value AI and analytics opportunities • Build reusable data products, semantic layers, and self-service analytics capabilities • Support AI initiatives involving LLMs, RAG architectures, vector databases, and enterprise knowledge systems • Collaborate with software engineering teams to operationalize analytics and AI capabilities in production environments • Contribute to the development of intelligent platform features that improve customer experience and operational efficiency • Serve as a trusted advisor on data strategy, architecture, and analytics best practices • Lead technical design reviews and architecture discussions • Mentor data scientists, data engineers, and analysts • Communicate technical concepts and recommendations to both technical and non-technical audiences.

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