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

• Lead and contribute to end-to-end Databricks implementations for clients, including data migration, Lakehouse architecture, and pipeline development • Gather technical requirements, design solutions, and present recommendations to client stakeholders (technical and business) • Build scalable ETL/ELT pipelines using PySpark, Delta Lake, Delta Live Tables (DLT), and Databricks Workflows • Design and implement Databricks Genie • Design and implement semantic layers • Use Databricks AI features to accelerate development, debugging, and code optimization • Design and implement Lakebase architectures for operational and analytical workloads, including transactional data use cases • Develop solutions using SDLC best practices, including modular code design, testing, and documentation • Use Git based version control with proper branching strategies • Implement CI/CD pipelines for Databricks asset • Implement data quality checks, validations, and expectations within workflows • Design and implement Unity Catalog governance, security, and lineage solutions • Optimize Databricks workloads for performance, cost, and reliability (Photon, cluster policies, Liquid Clustering, Auto Loader, etc.) • Integrate Databricks with client ecosystems (Azure, AWS, GCP, Snowflake, Kafka, legacy systems, etc.) • Support client workshops, proof-of-concepts (POCs), and knowledge transfer sessions • Deliver projects following consulting methodologies while meeting quality, timeline, and budget expectations • Document architectures, runbooks, and best practices for client use • Participate in solutioning activities (scoping, estimation, technical demos) as needed

• Guide clients on optimizing their data environment to work most effectively and efficiently for them. Clarify management objectives through data solutions. • Develop system engineering, integrations, and architectures based on client needs. • Implement and provide advice on data warehouse solutions, ETL pipelines, and business intelligence reporting tools. • Develop a data model around stated use cases to capture client’s KPIs and data transformations. Validation and testing of data models. • Teach technical data modeling concepts to a variety of audiences, including developers, data architects, business users, and IT professionals. • Support, maintain, and document clients’ data environments. • Work within a project management framework to meet objectives, understand scope, and impact of your work across an organization. • Work with the Sales and Marketing teams to develop Thought Leadership and support our sellers by talking to clients about DAS42’s capabilities.