BI Analytics Engineer

LED FastStart•Raceland, LA

About The Position

The BI/Analytics Engineer develops enterprise dashboards, semantic models, reporting layers, and self-service analytics capabilities to support operational and executive decision-making across Bollinger Shipyards. The role works closely with business stakeholders, analytics leadership, and data engineering teams to translate business requirements into scalable, user-friendly analytics solutions. The engineer designs and maintains Power BI reporting solutions, establishes KPI definitions and reporting standards, and supports reliable data availability, performance, usability, and governance. The role also helps users adopt self-service analytics, documents reports and data definitions, troubleshoots reporting issues, and validates data accuracy.

Requirements

Bachelor’s degree in Information Systems, Analytics, Computer Science, Business, or a related field.
5–8 years in BI or analytics engineering.
Strong Power BI and SQL skills.
Experience with enterprise reporting.

Nice To Haves

Experience with Azure analytics technologies and cloud data platforms.
Experience in manufacturing, industrial, operational, or engineering environments.
Familiarity with data warehousing and medallion architecture concepts.
Experience supporting executive reporting and KPI management.
Knowledge of data governance and metadata management practices.
Exposure to Python or other analytical programming tools.

Responsibilities

Design, develop, and maintain Power BI dashboards, reports, and datasets for enterprise operations and leadership reporting.
Build and manage semantic models, KPI definitions, and reporting standards.
Translate business requirements into scalable analytics and visualization solutions.
Develop reporting solutions that balance usability, performance, scalability, and governance requirements.
Collaborate with Data Engineering teams to ensure reliable and accurate data availability.
Partner with business stakeholders to define metrics, reporting requirements, and analytical needs.
Optimize dashboard performance, data models, and report usability.
Support enterprise self-service analytics capabilities and user adoption.
Maintain documentation for reports, data definitions, and analytics standards.
Assist with troubleshooting reporting issues and validating data accuracy.
Promote consistent analytics practices and reporting governance across business areas.