Director of Business Intelligence and AI Analytics

We are seeking a hands-on technical leader to build and deploy the dashboards, reports, and AI-enabled analytics that help senior leadership understand operational performance, identify risks, and make informed decisions.

We have an established database foundation supported by data entry and governance processes. This role will build on that foundation by turning trusted data into reliable reporting products that people use in their daily work. The position will integrate information from Oracle Primavera Cloud (OPC), our scheduling platform, Oracle Primavera Unifier, and Oracle APEX applications and their underlying data sources.

The successful candidate will personally write queries, develop reporting models, build dashboards, connect systems, and deploy solutions into production. They will also establish reporting priorities and technical standards, working closely with business leaders, system owners, and technical teams. The role carries substantial technical ownership and can operate as a senior individual contributor leading cross-functional delivery.

Strong technical execution must be paired with analytical judgment and clear communication. This person should be able to investigate a discrepancy in the underlying data, explain its business significance, and deliveran accurate, concise view for executives.

• Design, build, and deploy executive and operational dashboards that provide consistent views of schedules, milestones, delivery progress, dependencies, and emerging risks.
• Translate business questions into defined metrics, data requirements, reporting logic, and intuitive visualizations with useful filters and drill-down capabilities.
• Develop reusable datasets, SQL queries, and reporting models that support multiple dashboards and reduce manual report preparation.
• Own reporting solutions through requirements, development, testing, deployment, user adoption, and ongoing maintenance.

• Connect OPC, Unifier, and APEX-related data through supported APIs, database views, approved extracts, or existing integration pipelines, working with system owners to determine the appropriate approach.
• Reconcile project identifiers, reporting periods, status definitions, and data granularity across systems so combined reportsremainaccurate and meaningful.
• Build or extend automated data transformations and refresh workflows; investigate failed loads, stale data, and integration issues with the appropriate technical teams.

• Apply AI models, including large language models where appropriate, to practical reporting needs such as summarizing operational updates, explaining variances, and helping users explore approved data.
• Use Python, model APIs, and other appropriate tools to prototype, evaluate, and deploy repeatable analytical workflows
• Ground AI-generated insights in verifiable source data, test output quality against representative examples, and establish human review where needed.
• Assess model accuracy, reliability, cost, and business usefulness before expanding a solution; protect confidential information through approved tools and access controls.
• Use AI-assisted development effectively while independently reviewing and testing generated code, queries, calculations, and conclusions.

• Build on existing governance processes to maintain documented metric definitions, calculation rules, source mappings, ownership, and data lineage.
• Validate dashboard results against source systems and approved business definitions before release; make refresh timing and data limitations visible to users.
• Implementappropriate access controls, version control, release documentation, and monitoring so reporting products remain secure and supportable.
• Optimize query and dashboard performance, resolve defects, and work with data owners to address recurring quality issues at their source.

• Work with executives and operational leaders to identify the decisions reporting must support and prioritize a practical delivery roadmap.
• Present the few insights, risks, and actions that matter most, with enough supporting detail to answer follow-up questions.
• Develop working fluency in data center operations, infrastructure deployment, hardware testing, commissioning, and server turn-up to interpret operational information accurately.
• Lead cross-functional delivery with clear owners,…