Data Engineer 2 (Full-Time) - Special Projects Department

Design and maintain scalable data solutions that enable analytics, AI-driven insights, and decision-making across temple construction and operations . This role combines data engineering and analytics engineering , supporting data pipelines, AI-ready datasets, and business-ready insights in partnership with business operations teams Bachelor's degree in a related field or equivalent field experience 3-5 years in data engineering, analytics engineering, or related field, supporting property development and facility operations data Experience working with AI/ML concepts, data preparation for models, or supporting predictive analytics use cases SQL (advanced querying, transformations, performance tuning) Python (or similar) for data processing, automation, and basic ML integration ETL/ELT pipeline development (batch and/or streaming) Cloud data platforms (AWS, Azure, or GCP) Data modeling (dimensional/star schema) Data transformation frameworks (e.g., dbt or similar) BI and reporting tools (Power BI, Tableau, or similar) Exploratory data analysis (EDA), KPI/metric development, and AI-ready 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 development, construction, and operations Create and improve dashboards and reports for tracking performance (Power BI, Tableau etc.) Work with 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 → construction → operations → 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 (e.g., maintenance, cost, energy forecasting)