Analytics Engineer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Designs, develops, and maintains complex, Python‑based analytical applications that support decision‑making, reporting, and long‑term enterprise planning
• Leads the development of scalable architecture, modular codebases, and maintainable application components
• Implements secure software engineering practices, version control, testing frameworks, and code review processes to ensure high reliability, usability, and long‑term sustainability
• Serves as the primary software engineer for enterprise tools such as the Enterprise Capacity Analysis (ECA) application and related analytical platforms
• Builds and maintains data ingestion, transformation, validation, and integration pipelines that support analytical applications
• Develops and optimizes SQL queries, relational database schemas, and data access patterns to support performance‑critical workloads
• Ensures data structures and workflows align with evolving analytical and programmatic requirements across the nuclear security enterprise
• Develops user‑facing analytical interfaces, dashboards, visualizations, and interactive tools that help analysts and leadership understand complex datasets, review model outputs, assess scenarios, and generate insights
• Designs intuitive interfaces using Python‑based frameworks and visualization libraries to translate analysis into actionable information for decision‑makers
• Ensures application usability, accessibility, and responsiveness
• Supports program planning, evaluation, and performance tracking by conducting cost, schedule, and risk analyses
• Collaborates with various NNSA offices (NA-11, NA-15, NA-18, NA-19) and stakeholders across the nuclear security enterprise to develop standardized analytical workflows and support future analysis needs
• Prepares program cost and budget documentation over several budget fiscal years, including the development of complex spreadsheets, development of program planning and budget briefings, and draft preparation of work authorizations and approved funding program documentation
• Provides program management support in the preparation, coordination, evaluation, review and analysis of program execution plans and tracking, monitoring, and reporting of program-specific milestones and updates of annual performance measures

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Analytics Engineer

Software Engineer, Analytics