Job DescriptionThe purpose of this position is to "seek to bring forth and establish the cause of Zion."
Design, support, and maintain the engineering systems and data that enable analytics, AI-driven insights, and decision-making across temple construction and operations. This role combines engineering and data/analytics engineering, supporting data pipelines, AI-ready datasets, and business-ready insights in partnership with business operations teams.
Responsibilities- Support engineering systems and design processes that generate data for project and property operations
- Build and maintain data pipelines from project and property systems
- Support cloud data platforms and data storage environments
- Organize data to support reporting across assets and projects
- Combine and structure data from engineering, construction, and operations
- Create and improve autonomous enterprise dashboards and reports for tracking performance
- Work with engineering, construction, and facilities teams to deliver data solutions
- Ensure data is accurate, consistent, and reliable
- Improve data processes for speed and efficiency
- Support lifecycle data tracking (design 12 construction 12 operations 12 maintenance)
- Establish documentation, standards, and data engineering best practices
- Prepare clean, usable datasets for analytics and AI use
- Perform exploratory data analysis to identify trends, anomalies, and insights
- Help define and standardize key performance metrics (KPIs)
- Collaborate on predictive and AI use cases
Qualifications- Bachelor's degree in Engineering or a related field, or equivalent field experience
- 3-5 years in engineering, with experience in data engineering, analytics engineering, or related work supporting property development and facility operations data
- Experience working with AI/ML concepts, data preparation for models, or supporting predictive analytics use cases
Technical- Agentic AI architecture and orchestration (multi-agent systems, tool/function calling, MCP or comparable agent-to-system protocols)
- Autonomous enterprise solution design - workflows where AI agents execute, not just recommend, across business systemsETL/ELT pipeline development (batch and/or streaming) feeding agentic and analytics workloads
- Governance and guardrails for AI-driven and autonomous systems (data quality, audit trail, human-in-the-loop controls)
- Exploratory data analysis (EDA), KPI/metric development, and AI-ready dataset preparation
