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BI Analytics Engineer

Bollinger Shipyards
Raceland, USA
Full-time
Director
Onsite
Discovered 5 days ago
Power BISQLBusiness intelligenceAnalytics engineeringEnterprise reportingDashboard development

Full Job Posting

Position overview

Develop enterprise dashboards, semantic models, reporting layers, and self-service analytics for operational and executive decision-making.

Translate business requirements into scalable, user-friendly analytics solutions with stakeholders, analytics leadership, and data engineering teams.

The description lists the location as multiple locations.

Key responsibilities

  • Develop and maintain Power BI dashboards, reports, and datasets for operations and leadership reporting.
  • Build semantic models, KPI definitions, and reporting standards.
  • Translate business requirements into scalable analytics and visualization solutions.
  • Balance usability, performance, scalability, and governance in reporting solutions.
  • Work with Data Engineering to support reliable, accurate data availability.
  • Define metrics, reporting requirements, and analytical needs with business stakeholders.
  • Optimize dashboard performance, data models, and report usability.
  • Support self-service analytics and user adoption.
  • Document reports, data definitions, and analytics standards.
  • Troubleshoot reporting issues and validate data accuracy.
  • Promote consistent analytics practices and reporting governance.

Qualifications

  • Bachelor’s degree in Information Systems, Analytics, Computer Science, Business, or a related field.
  • Five to eight years of BI or analytics engineering experience.
  • Strong Power BI and SQL skills.
  • Experience with enterprise reporting.

Additional skills and experience

  • 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.

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