Data and AI Engineer

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field, or equivalent experience.


• 5+ years of relevant work experience in data engineering, software development, or systems integration, with exposure to the requirements, processes, and functions of general contracting preferred.

OR

• 3+ years of experience with direct responsibility for building and maintaining data pipelines, integrations, or automation solutions, including:
  
  
  
  • Designing and building ETL/ELT pipelines and data integrations.
  
  
  • SQL and Python (or a similar language) for data transformation and automation.
  
  
  • Integrating enterprise systems through REST APIs (Procore, Viewpoint Vista, UKG, or similar ERP/project management platforms preferred).
  
  
  • Cloud data platforms such as Microsoft Fabric, Azure Data Factory, Azure SQL, or Synapse.
  
  
  • Data modeling, data quality, and governance practices.
  
  
  • Building AI-enabled automations or agents using LLM platforms, Power Automate, or similar tools.
  
  
  • Version control (Git) and deployment practices for production systems.
  
  
  
  

LICENSES / CERTIFICATIONS

• One or more of the following, or equivalent (preferred):


• Microsoft Certified: Power BI Data Analyst Associate (PL-300).


• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600).


• Microsoft Certified: Azure Data Fundamentals (DP-900).


• Microsoft Certified: AI Business Professional (AB-730).

KNOWLEDGE

• Strong working knowledge of construction operations practices, processes, and commercial construction project context (preferred).


• Strong working knowledge of the information required by preconstruction and construction operations in day-to-day decision-making and in the creation of schedules, estimates, and budgets (preferred).


• SQL and DAX, including working with semantic models and star schemas.


• Microsoft Fabric and Power BI Service (preferred).


• Data governance concepts, including data ownership, data quality, metadata, and data classification.


• Practical use of AI tools for analysis, and awareness of their limits (preferred).


• Agile development framework.

SKILLS

• Process improvement through automation: Finds manual, repetitive workflows across operations, preconstruction, and the field, and replaces them with reliable automated solutions.


• Project planning and delivery: Breaks pipeline, integration, and agent projects into milestones, ships working pieces in stages, and manages priorities across competing requests.


• Engineering judgment: Designs data pipelines and AI workflows that are maintainable, monitored, and secure. Decides when to build, when to buy, and when not to automate.


• Problem solving: Troubleshoots data quality issues, failed integrations, and unexpected model or agent behavior by finding the root cause instead of applying patches.


• Ownership and reliability: Treats production pipelines and automations as services. Owns them end to end, from monitoring and alerting through fixes.


• Responsible AI and data governance: Builds with security, access controls, and compliance in mind, including handling CUI and sensitive project data appropriately.


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  Communication skills:

  
  
  
  
  • Technical documentation: Writes clear runbooks, data definitions, and architecture notes so others can support and extend the work.
  
  
  • Requirements and collaboration: Turns loosely defined business needs from non-technical stakeholders into working solutions, and partners closely with the data analyst, who uses the data this role produces.
  
  
  
  

ABILITIES

• Demonstrate critical and holistic thinking.


• Ability to communicate comfortably and effectively with new and diverse groups of people.


• Ability to influence others and lead change.


• Ability to critically analyze complex and varied information to make recommendations.


• Ability to leverage technology to facilitate communication and collaboration.


• Comfortable collaborating across functions to identify data analytics problems and execute solutions with technical rigor and data-driven insights.