Data and AI Project Analyst

Data & AI Project Analyst

Location: Monroe, LA
Type: On-site

The Data & AI Project Analyst serves as the field-facing connector between project teams, account leadership, owners/JV partners, and DPR's Technology & Innovation groups—translating business needs into scalable data, analytics, integration, and AI solutions. This role engages early to shape requirements, standardize approaches across projects, coordinate delivery with U.S. and offsite teams, and ensure all data sharing and AI use aligns with governance, legal, and contractual obligations.

This is a jobsite-based role, which will require regular travel between all jobsites within a national account.

• Engage early in pursuit and preconstruction to identify owner‑mandated technologies, capture data requirements and reporting obligations, surface integration needs and constraints, and identify AI opportunities.
• Partner with Integration Managers, Account Leadership, Project Teams, other Account leads, and other T&I Groups (CT, IT, ETS) to align on scalable and repeatable approaches.
• Align project‑level data needs with DPR's Data Strategy and enterprise standards, delivering consistent, flexible solutions that drive measurable impact across the account.
• Translate business and project needs into clear data, analytics, and integration requirements. This role is primarily field‑based, with approximately 75% of time spent on active jobsites and limited opportunity for remote work, including participation in key meetings and workgroup meetings at the jobsite.
• Align AI use cases with owner expectations and contract constraints; advise on feasibility and value of AI‑driven solutions.

• Influence strategic technology decisions related to data, analytics, AI, and development.
• Lead conversations with owners, JV partners, and stakeholders on data exchange approaches, including system access vs data sharing, file‑based vs platform‑based integrations, and reporting vs operational use cases; guide teams through custom analytics and development.
• Coordinate with Data Engineering, Solution Architecture, Analytics, and offsite teams to define integration approaches, ensure feasibility and scalability (avoiding one‑off solutions), and act as a funnel for requests with US and offsite teams.
• Manage UAT and QA/QC for deliverables, collaborating with U.S. and offsite teams to incorporate feedback, and own final production readiness and quality.
• Drive data readiness and integration strategies to support scalable pipelines and enable effective consumption of predictive and generative AI models.
• Support implementation of standardized data exchange frameworks and templates; ensure all external data sharing aligns with data governance, legal, and contractual requirements.
• Provide hands‑on support in analytics and Power BI, iterating on reports, making minor updates, and developing proof‑of‑concept solutions based on real‑time user feedback.

• Act as the front door for data and development requests at the account level; work with Data & Development Lead - Mega Projects for prioritization across accounts.
• Ensure requests are clearly defined, properly scoped, and prioritized based on business impact.
• Coordinate execution across Data Engineering, Data Analytics, AI/ML, and Software Development; add AI‑specific intake criteria (value, risk, data readiness).
• Prioritize AI initiatives alongside analytics and development work; coordinate across AI/ML teams for model development and deployment.
• Track progress, manage expectations, and communicate updates to stakeholders; escalate risks, conflicts, and capacity constraints when needed.

• Identify opportunities to reuse existing dashboards, pipelines, and integrations; avoid duplication across projects and accounts.
• Promote standardized approaches for data mapping, integration patterns, and reporting structures; drive implementation of AI use cases by prioritizing reusable models, prompts, and workflows (minimizing one‑off, non‑scalable solutions).
• Contribute to the development of templates and best practices for mega projects.