Python Automation Developer Jobs in Richardson, TX

Kiewit is seeking a Digital Model / BIM Automation Developer to support the processing, transformation, and delivery of engineering models across enterprise-scale EPC projects. This role is responsible for managing and scaling automated pipelines that deliver critical 3D model data to thousands of users daily.

This is a highly technical, operations-focused role within the Digital Project Execution (DPE) team, requiring strong programming, data handling, and systems expertise. The position plays a key role in ensuring reliability, scalability, and performance of model delivery systems, while continuously improving automation processes to support rapid organizational growth.

This position is within the Kiewit Technology Group, supporting Kiewit Nuclear Solutions. 

This is a remote position that may require some travel. 

Model Processing & Delivery

• Process, convert, and prepare engineering models for downstream use in UniversalPlantViewer (UPV) / Universal Digital Twin Hub / UDITH
• Manage and support the daily delivery of 100+ models to 3,000+ users across projects
• Ensure model quality, consistency, and availability across environments
• Troubleshoot model processing failures and implement corrective actions

Automation & Optimization

• Develop, enhance, and maintain automation pipelines for model processing and delivery
• Identify opportunities to reduce manual intervention and improve system reliability
• Optimize existing workflows for performance, scalability, and maintainability
• Implement monitoring and alerting for critical automation processes

Systems & Infrastructure Support

• Support general systems administration tasks related to model processing environments
• Maintain and improve infrastructure supporting automated workflows
• Ensure systems are stable, secure, and performant under increasing scale

Data & Integration

• Work with structured data using SQL to support model metadata and integration workflows
• Integrate model data with downstream systems and platforms (e.g., Common Data Environments, analytics tools)
• Support data transformation and validation processes

Continuous Improvement & Scaling

• Contribute to scaling operations in a rapidly growing environment (~5x growth over recent years)
• Improve reliability and maintainability of model delivery systems
• Support onboarding of new projects and increased model volumes

Technology stack

Primary:

• Universal Plant Viewer (UPV)
• Python (preferred)
• SQL Server / Azure SQL
• Model processing tools and formats (e.g., SmartPlant Review, Navisworks, 3D model platforms)
• Automation frameworks / scripting

Supporting:

• Azure-based ecosystem (Data Factory, Databricks, Data Lake)
• CI/CD and scripting tools

Required qualifications

• Experience processing and converting engineering models
• Strong proficiency in a modern programming language (Python preferred)
• Engineering or construction industry experience (EPC environment preferred)
• Strong SQL skills for data manipulation and integration
• Proven ability to automate complex processes and workflows
• Strong systems and infrastructure fundamentals (systems administration experience preferred)
• Highly proficient in problem solving and troubleshooting complex technical issues
• Strong foundational knowledge of computers, data structures, and coding principles
• Ability to quickly learn new tools, systems, and workflows
• Strong sense of ownership and accountability for delivering business-critical systems
• Experience supporting high-availability systems used by large user bases (thousands of users)
• Experience documenting functional requirements, technical designs, and data workflows.
• Excellent written and verbal communication skills.

PREFERRED QUALIFICATIONS

• Background in data analytics or data engineering
• Experience training or supporting end users

BONUS EXPERIENCE

• Knowledge of engineering design tool ecosystems (Hexagon Smart Suite).
• Experience supporting data environments tied to project lifecycle systems.
• Exposure to large-scale project-based data environments.