Grease Monkey International - AI BI Developer

Developer is needed in Greenwood Village, United States.

Client: Grease Monkey International

Location: Greenwood Village, CO

Contract: undefined

Job Description

We are seeking a forward-thinking Business Intelligence Developer who can design, build, and evolve data and analytics solutions that drive decision velocity and business impact. This role is not about producing static reports - it’s about shaping an AI-augmented analytics ecosystem that empowers every tier of the organization to make smarter, faster, and more confident decisions.

The successful candidate will be measured not by the number of dashboards created, but by the insights generated, the adoption achieved, and the tangible business outcomes enabled.

Requirements

The ideal candidate combines deep BI expertise with a pioneering mindset for AI integration. They are equally comfortable designing complex data models and explaining them in plain English to executives. They think in terms of outcomes, not outputs, and measure success by the change they enable in the business.

Candidate should have 5 – 9 years of experience in BI/AI development.

Key Desired Outcomes

• Accelerated Decision-Making
• Within six (6) months, business users can move from question to a decision minutes/hours versus days/weeks, across our Invoice-level detail and customer detail.
• Integrate AI-powered analytics to surface proactive insights before stakeholders ask.
• High-Trust, High-Adoption Analytics
• Within nine (9) months, identify, develop, and monitor at least 80% of key AI/BI outputs, promoting their use by decision makers.
• Monitor user adoption rates, measured and reported monthly, promoting steady increases across all functions (Tableau or similar BI tool and ChatGPT Enterprise use).
• Seamless AI & Data Integration
• By 6 months, all BI pipelines and reports have monitoring and alerting in place.
• Integrate generative AI tools to enable natural-language queries, predictive forecasts, and prescriptive recommendations.
• Sustainable Analytics Ecosystem
• Create a BI environment that scales without ballooning complexity—systems that are easy to maintain, enhance, and govern.
• Implement governance and AI model monitoring to ensure compliance, ethical use, and data privacy.
• Measurable Business Impact
• Directly influence KPIs such as revenue growth, operational efficiency, and customer satisfaction by embedding analytics into workflows.
• Demonstrate ROI on analytics projects within defined timeframes.

Typical Job Functions

• Data Modeling & Engineering: Develop and optimize ETL/ELT pipelines, semantic layers, and data models for BI and AI applications.
• Dashboard & Visualization Development: Design interactive dashboards and reports (e.g., Power BI, Tableau, Looker) that enable intuitive exploration of data.
• AI/ML Enablement: Build predictive models into BI tools and workflows; operationalize AI for business-facing use cases.
• Robotic Process Automation (RPA): Design, develop, and maintain automation workflows (e.g., Power Automate, UiPath, Automation Anywhere).
• Data Governance & Quality: Ensure accuracy, lineage, and compliance across BI assets; implement monitoring for AI-generated outputs.
• Collaboration & Consulting: Work with business units to translate needs into analytics solutions, acting as a bridge between technical and business teams.
• Performance Optimization: Continuously tune BI systems for scalability, reliability, and speed.
• SQL/Python expertise: Write complex queries and scripts, optimize performance, and manage large datasets across multiple sources for data analysis, automation, and integration with AI/ML workflows.
• Innovation & Experimentation: Research and pilot emerging AI/BI technologies that could provide competitive advantage.

Success in this Role Looks Like

• Stakeholders across the enterprise are using AI-enhanced BI tools daily to inform decisions.
• Predictive and prescriptive analytics are delivering measurable improvements in at least three core business KPIs.
• The organization has reduced reliance on static reports by at least 50%, with conversational and automated analytics becoming standard.
• Governance frameworks ensure AI transparency, explainability, and compliance without slowing down delivery.
• Near term (within the first 60 days) – Identify ways to improve the data ingest process from key data sources by developing systems and processes which increase resiliency, automation, and rapid recovery.
• Mid-term (within the first 120 days) – Improve integration and automation between training, operations, and human resources systems.
• Longer term (within the first 6 months) – Verify that all existing pipelines and reports have the proper monitoring and alerting applied to them and build a process that ensures monitoring and alerting are included in all new development efforts.