Business Intelligence Solutions Engineer

Job Purpose

The Business Intelligence (BI) Solutions Engineer turns organizational data into clear, trustworthy insights that help Liliʻuokalani Trust (LT) make informed decisions, improve services, and strengthen impact for Native Hawaiian youth and families. This role sits between traditional data engineering and BI analysis: it designs and maintains data models and transformation pipelines, writes and optimizes SQL queries, integrates data from multiple systems, and translates business questions into actionable insights.

The Solutions Engineer partners with leadership, program teams, IT, the data engineering team, and other stakeholders to understand needs, define requirements, and deliver analytical products that are accurate, accessible, and useful — in service of LT's mission to improve the wellbeing of Native Hawaiian youth.

The role also contributes to LT's evolving use of automation and AI to efficiently deliver insights to decision-makers. The Solutions Engineer is expected to experiment responsibly with AI-assisted workflows, prototype practical solutions, and evaluate where new tools can improve analytical speed, quality, accessibility, and adoption — while maintaining strong human judgment, data privacy, governance, and accountability for final outputs.

Success in this role will be measured by:

• Accuracy, timeliness, and adoption of analytic products;
• Effectiveness of requirements analysis and alignment of data solutions with business needs;
• Documented instances of analytic products informing strategic planning or program decisions;
• Insights delivered that lead to a measurable process, program, or strategic change; and
• Effective collaboration in developing AI-assisted tools into analytic workflows, measured by efficiency gains and continued accuracy/reliability of outputs.

• Design, develop, and maintain dashboards, scorecards, and reports that communicate key performance indicators, business trends, and actionable insights.
• Write and optimize SQL queries against the data warehouse to answer ad hoc business questions and support recurring reporting needs.
• Manage the lifecycle of analytics products, including intake, requirements gathering, development, testing, documentation, adoption support, maintenance, and retirement when needed.

• Build and maintain data models and transformation pipelines that turn raw source data into reliable, analysis-ready datasets, partnering with the data engineering team on upstream infrastructure while owning the transformation and modeling layer that feeds BI products.
• Combine data from multiple systems, including data warehouses, CRM platforms, spreadsheets, and other operational sources, into reliable, analysis-ready datasets.
• Identify, investigate, and resolve data quality issues by understanding source systems, business logic, transformation rules, and data lineage.
• Support process automation and reporting improvements that reduce manual work, improve consistency, and strengthen trust in organizational data.

• Prototype and test AI-enabled tools, agents, or assistants that help teammates ask questions of data, generate insights, or streamline analytical workflows.
• Use AI-assisted tools to support tasks such as SQL drafting, analytical prototyping, documentation, summarization, anomaly detection, and recurring report generation.
• Evaluate AI experiments on real use cases, documenting what worked, what did not, risks identified, and whether the solution is appropriate to scale.
• Apply responsible AI practices — including awareness of model limitations, hallucination risk, data privacy, and governance requirements — and…