Staff Analytics Engineer

The Nuclear Company•Seattle, WA

About The Position

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.

Requirements

Bachelor’s degree in computer science, Data Science, Statistics, Engineering, or related field (Master's preferred)
7+ years of experience in analytics engineering, data engineering, or business intelligence
3+ years working with enterprise data platforms and analytics systems
Experience in construction, industrial, or complex project environments preferred
Expert proficiency in SQL and database technologies (PostgreSQL, Snowflake, BigQuery, or similar)
Strong experience with ETL/ELT tools (Apache Airflow, Apache NiFi, dbt, or similar)
Proficiency in data modeling techniques (dimensional modeling, data vault, etc.)
Experience with data warehousing and data lake architectures
Knowledge of data pipeline orchestration and workflow management
Understanding of data governance, quality, and lineage
Strong skills in data analysis, statistical modeling, and data visualization tools
Expert proficiency in BI tools (Tableau, PowerBI, Looker, or similar)
Experience with business analytics and KPI development
Knowledge of statistical analysis and A/B testing
Understanding of data storytelling and visualization best practices
Strong programming skills in Python and/or Scala
Experience with Palantir Foundry or similar enterprise data platforms
Proficiency in version control (Git) and CI/CD practices
Knowledge of cloud platforms (AWS, Azure, GCP)
Familiarity with containerization (Docker, Kubernetes)
Understanding of API development and microservices
Understanding of construction project management and workflows
Knowledge of engineering data, BIM models, and project controls
Familiarity with IoT data, sensor networks, and real-time analytics
Awareness of nuclear industry requirements (preferred)
Understanding of supply chain, procurement, and compliance data
Strong analytical and problem-solving skills with ability to translate business problems into analytics solutions
Excellent communication skills to explain complex data concepts to technical and non-technical stakeholders
Ability to work independently and lead analytics initiatives
Collaborative mindset to work across teams and functions
Attention to detail with focus on data quality and accuracy
Strategic thinking to align analytics with business objectives

Nice To Haves

Master's degree or Ph.D. in Data Science, Statistics, or related field
Experience with Palantir Foundry platform
Background in machine learning and AI systems
Knowledge of real-time streaming analytics (Kafka, Flink, Spark Streaming)
Experience with graph databases and network analysis
Familiarity with blockchain and distributed ledger data
Certifications in data engineering or analytics (AWS, GCP, Databricks)
Experience in nuclear, energy, or highly regulated industries

Responsibilities

Data Architecture & Modeling
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
Data Pipeline Development
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
Analytics & Business Intelligence
Create dashboards and analyses that demonstrate Nuclear OS's value using BI tools (Tableau, PowerBI, 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
AI/ML Support & Predictive Analytics
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
Cross-Functional Collaboration
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
Platform Development & Optimization
Build analytics infrastructure on Palantir Foundry and associated platforms 17
Optimize data pipelines for performance, cost, and reliability
Implement data observability and monitoring systems
Ensure scalability of analytics systems for fleet-wide deployment
Develop APIs for analytics services and data access
Create reusable analytics components and templates
Technical Leadership & Mentorship
Provide technical guidance and mentorship to junior analytics engineers, fostering their growth and ensuring adherence to best practices
Establish analytics engineering standards and best practices
Lead code reviews and technical design discussions
Drive continuous improvement in analytics processes and tools
Share knowledge through documentation and training