Data Engineer - Data & AI Applications

Capital Project Intelligence is a full-service consultancy and professional services firm specializing in large capital projects. The Data Engineer will support analytics, reporting, data workflows, and AI-enabled application development, collaborating with technical leads and stakeholders to deliver reliable solutions.

Responsibilities:

• Build and maintain data workflows, pipelines, automations, and application components that support analytics, reporting, and AI enabled use cases
• Support the development of AI enabled tools, prototypes, and applications by preparing data, integrating APIs or AI services, and implementing reusable technical components
• Collaborate with stakeholders and the Technical Lead to understand data, reporting, application, and AI workflow requirements and translate them into practical technical solutions
• Design, monitor, and optimize ETL processes, data workflows, and automation scripts for reliability, accuracy, and maintainability
• Develop and enforce data validation, lineage, quality checks, and documentation across reporting, analytics, and AI application workflows
• Ensure data is organized, clean, secure, and accessible for analytics, reporting, dashboarding, and application use cases
• Build, maintain, or support Power BI data models, dashboards, reports, and other business facing data products as needed
• Create and maintain documentation of pipeline logic, data flow, code, prompts, assumptions, dependencies, and technical decisions
• Support lightweight user facing tools, prototypes, interfaces, or interactive outputs that help users consume data and AI generated results effectively
• Work with the Technical Lead to scope assigned work, clarify requirements, identify risks, and align technical decisions with broader project and system priorities
• Use version control, automation, reusable code patterns, and development best practices to support maintainable delivery
• Communicate progress, blockers, technical findings, and tradeoffs clearly to team members, stakeholders, and project leadership
• Proactively identify opportunities to improve reporting processes, data workflows, AI application components, documentation, and delivery efficiency
• Support data integrity, security, quality, and governance expectations throughout analytics, reporting, and application development work
• Accurate and timely timekeeping

Requirements:

• Strong technical problem-solving skills
• Hands-on experience with SQL
• Hands-on experience with Python
• Ability to support business-facing reporting and visualization needs
• Capability to help build practical AI-enabled tools, prototypes, automations, and data applications
• Versatile technical contributor who can work across data engineering, analytics, and application development
• Collaboration skills with technical leads, stakeholders, and cross-functional teams
• Experience in building and maintaining data workflows, pipelines, automations, and application components
• Experience in supporting the development of AI enabled tools, prototypes, and applications
• Ability to collaborate with stakeholders to understand data, reporting, application, and AI workflow requirements
• Experience in designing, monitoring, and optimizing ETL processes, data workflows, and automation scripts
• Experience in developing and enforcing data validation, lineage, quality checks, and documentation
• Ability to ensure data is organized, clean, secure, and accessible for analytics, reporting, dashboarding, and application use cases
• Experience in building, maintaining, or supporting Power BI data models, dashboards, reports, and other business facing data products
• Ability to create and maintain documentation of pipeline logic, data flow, code, prompts, assumptions, dependencies, and technical decisions
• Experience in supporting lightweight user facing tools, prototypes, interfaces, or interactive outputs
• Ability to work with the Technical Lead to scope assigned work, clarify requirements, identify risks, and align technical decisions
• Experience in using version control, automation, reusable code patterns, and development best practices
• Ability to communicate progress, blockers, technical findings, and tradeoffs clearly
• Proactive identification of opportunities to improve reporting processes, data workflows, AI application components, documentation, and delivery efficiency
• Support for data integrity, security, quality, and governance expectations throughout analytics, reporting, and application development work
• Accurate and timely timekeeping