Analytics Engineer

The Nuclear Company is the fastest growing startup in the nuclear and energy space creating a never before seen fleet‑scale approach to building nuclear reactors. Through its design‑once, build‑many approach and coalition building across communities, regulators, and financial stakeholders, The Nuclear Company is committed to delivering safe and reliable electricity at the lowest cost, while catalyzing the nuclear industry toward rapid development in America and globally.

The Staff Analytics Engineer is a senior technical role responsible for designing, building, and maintaining the data infrastructure, analytics pipelines, and business intelligence systems that power Nuclear OS. This position combines expertise in data engineering, analytics, and business intelligence to transform raw data into actionable insights that drive decision‑making across nuclear construction projects. You'll work at the intersection of data engineering and analytics, building sophisticated data models, ETL/ELT pipelines, and visualization tools that enable predictive analytics, real‑time monitoring, and AI‑driven optimization for nuclear project delivery.

• Lead the design and implementation of sophisticated data models in data warehouses/lakes, optimizing for performance, scalability, and ease of consumption by analytics tools and data scientists
• Design data storage and analytics systems that support predictive maintenance and business intelligence goals
• Build unified data ontology that creates a "digital twin" of nuclear projects integrating diverse data sources
• Create centralized data lake for all project data including construction performance metrics, quality incidents, schedule deviations, and cost data
• Develop data governance frameworks and quality assurance protocols for enterprise data

• Develop ingestion pipelines for diverse datasets using ETL/ELT tools (Apache NiFi, Apache Airflow)
• Build automated data pipelines integrating Primavera schedules, BIM models, IoT sensor telemetry, and other sources
• Perform complex data transformations, aggregations, and feature engineering to prepare data for advanced analytics, machine learning, and reporting
• Integrate real‑time data from IoT sensors, AR devices, and field operations into analytics systems
• Ensure data quality through validation, cleansing, and monitoring processes

• Create dashboards and analyses that demonstrate Nuclear OS's value using BI tools (Tableau, Power
  
  BI, Superset)
• Build business intelligence systems for data analysis and decision‑making
• Develop analytics tools tailored to project and operational needs
• Drive innovation in complex data visualization and project management interfaces that make nuclear construction data accessible and actionable
• Create standardized performance metrics and reporting frameworks
• Build real‑time dashboards with data connectivity for monitoring construction progress and operational performance

• Support predictive analytics and machine learning models that learn from historical and real‑time data
• Build data pipelines for training and running ML models at scale
• Enable AI‑driven predictive analytics for schedule optimization, risk detection, and anomaly identification
• Support AI models for predictive scheduling, ITAAC automation, and risk assessment
• Prepare feature‑engineered datasets for data scientists and ML engineers
• Monitor model performance and data drift for production ML systems

• Work closely with data scientists, principal/senior data engineers, software developers, and business leaders to understand complex data needs and deliver impactful solutions
• Collaborate with engineering, construction, and operations teams to translate business problems into analytics solutions
• Partner with product teams to define analytics requirements and success metrics
• Support regulatory and compliance teams with data‑driven insights
• Enable business leaders to make data‑driven decisions through accessible analytics

• Build analytics infrastructure on…