Engineering Lead, BI & Analytics

• Technical Solution Leadership: Own the BI and analytics solution strategy by defining architecture, standards, and roadmap for trusted reporting, semantic models, analytics products, data science solutions, and AI-enabled analytical experiences that support enterprise decision-making.
• AI-Enabled Analytics Evolution: Help move analytics beyond traditional dashboards by identifying opportunities for natural-language exploration, proactive insights, recommendations, anomaly detection, predictive analytics, and agentic analytics patterns where trusted data, context, and governance support reliable use.
• Stakeholder Partnership: Partner with business leaders, product owners, data engineering, platform teams, and AI stakeholders to translate business needs into scalable analytics and data science solutions aligned to measurable business outcomes.
• Delivery Planning: Translate business priorities into solution approaches, dashboard requirements, semantic model needs, analytical methods, implementation plans, and well-scoped work that enables effective team execution.
• Build scalable analytics products that combine strong data modeling, business context, intuitive user experience, reusable design patterns, and appropriate statistical or AI methods to enable trusted self-service, executive-ready insights, and more proactive decision support.
• Standards and Reusable Patterns: Set standards for dashboard design, semantic modeling, metric definitions, report performance, analytical product design, documentation, testing, deployment, and reusable delivery patterns that improve maintainability, trust, and speed.
• Data Science Leadership: Guide the data science lifecycle from business problem framing and analytical approach through model development, validation, operationalization, and adoption, ensuring predictive and AI-enabled solutions are reliable, explainable, and connected to measurable business outcomes.
• Team Leadership: Lead, coach, and develop a team of BI developers and data scientists, supporting prioritization, delivery execution, issue resolution, technical growth, and cross-discipline collaboration.
• Hands-On Engineering: Contribute directly to design review, code review, and resolution of complex engineering issues to help ensure solution quality, reliability, and production readiness.

Business Intelligence, Leadership, Data Science, Business Intelligence, Analysis Skills, Reporting Dashboards, Artificial Intelligence (AI), Standards Development, Decision Support, Predictive Modeling, * Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, Statistics, or a related field; advanced degree is a plus.

• 7+ years of experience across BI development, analytics engineering, data visualization, semantic modeling, BI architecture, data platforms, data science, applied analytics, or related technical roles, including experience leading technical teams or delivery efforts.
• Strong expertise in SQL, data modeling, semantic modeling, metric definition, and modern BI and analytics development practices.
• Practical fluency in data science concepts such as statistical analysis, predictive modeling, machine learning, experimentation, model evaluation, and responsible use of AI-enabled analytics.
• Experience designing and delivering business-critical dashboards, reports, analytics products, predictive insights, and self-service reporting capabilities for enterprise audiences.
• Experience with BI and analytics tools such as Power BI, Tableau, Looker, or equivalent technologies; Power BI experience is strongly preferred.
• Experience with modern cloud data platforms and warehouse or lakehouse ecosystems such as Databricks, Snowflake, BigQuery, Azure data services, or equivalent technologies.
• Experience applying AI-enabled analytics patterns such as natural-language data exploration, anomaly detection, forecasting, recommendations, analytical copilots, or agentic analytics is preferred.
• Experience establishing trusted analytics practices through governed metrics, data quality checks, validation, documentation, access controls, model evaluation, human oversight, and adoption support.
• Experience defining dashboard standards, semantic model conventions, analytical product patterns, documentation practices, deployment approaches, and reusable delivery patterns for enterprise analytics solutions.
• Experience translating business needs into clear requirements, analytics designs, data science problem framing, implementation plans, and well-scoped technical work.
• Strong communication and stakeholder management skills, with the ability to explain tradeoffs, align cross-functional teams, and connect analytics, data science, and AI-enabled solutions to business value.
• Construction industry experience is helpful but not required.