Overview
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Job Details
BI Enablement & Analytics Lead 100% Remote
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Job Description
We are seeking a strategic and hands-on BI Enablement & Analytics Lead to drive the adoption of data-driven decision-making across our organization. Sitting at the intersection of technical development and business strategy, you will be responsible for designing high-impact analytics and AI products while simultaneously building the "data culture" required to support them. You will move beyond simple reporting to act as a true partner to business units-translating complex data into compelling stories, mentoring teams on visualization best practices, and spearheading initiatives to improve data literacy and AI readiness enterprise-wide.
Key Responsibilities
Product Development & Strategy: Design, develop, and maintain advanced data, analytics, and AI products that solve specific business problems and make insights accessible to non-technical users.
Data Literacy & Training: Design and deliver engaging workshops, training courses, and educational materials to elevate data literacy and AI proficiency across the organization.
Stakeholder Partnership: Collaborate with cross-functional departments to gather requirements, identify process gaps, and deliver tailored analytics solutions that drive measurable business impact.
Mentorship & Best Practices: Guide analysts and business stakeholders on data visualization standards (UI/UX), analytics techniques, and the effective use of BI tools.
Culture & Storytelling: Engage with leadership and teams to "share data stories," promoting an integrated analytics-driven culture and encouraging collaboration.
Governance & Documentation: Develop and maintain robust documentation, guidelines, and governance frameworks for analytics processes, data visualization standards, and AI tool usage.
Performance Monitoring: Track and analyze the usage and business impact of data initiatives to measure success and identify areas for optimization.
Innovation: Stay current with emerging trends in Data Science, GenAI, and visualization technologies to continuously innovate internal capabilities.
Required Skills and Qualifications
Technical Modeling: Proven experience in dimensional data modeling, software analysis, design, and testing for enterprise data warehousing solutions.
Visualization Expertise: High-level proficiency in Tableau or Power BI is crucial for creating intuitive and impactful data visualizations.
AI & GenAI Proficiency: Essential knowledge of data science and machine learning concepts, with practical experience in Generative AI tools and prompt engineering.
Coding & Manipulation: Familiarity with SQL for data querying and programming languages like Python or R for manipulation and analysis.
Communication: Exceptional communication and presentation skills, with the specific ability to explain complex technical topics to non-technical audiences clearly.
Governance: A strong understanding of data quality, security, and governance principles.
Problem Solving: Demonstrated ability to identify root causes of data issues and implement process improvements.
Job Responsibilities
Product Development & Strategy: Design, develop, and maintain advanced data, analytics, and AI products that solve specific business problems and make insights accessible to non-technical users.
Data Literacy & Training: Design and deliver engaging workshops, training courses, and educational materials to elevate data literacy and AI proficiency across the organization.
Stakeholder Partnership: Collaborate with cross-functional departments to gather requirements, identify process gaps, and deliver tailored analytics solutions that drive measurable business impact.
Mentorship & Best Practices: Guide analysts and business stakeholders on data visualization standards (UI/UX), analytics techniques, and the effective use of BI tools.
Culture & Storytelling: Engage with leadership and teams to "share data stories," promoting an integrated analytics-driven culture and encouraging collaboration.
Governance & Documentation: Develop and maintain robust documentation, guidelines, and governance frameworks for analytics processes, data visualization standards, and AI tool usage.
Performance Monitoring: Track and analyze the usage and business impact of data initiatives to measure success and identify areas for optimization.
Innovation: Stay current with emerging trends in Data Science, GenAI, and visualization technologies to continuously innovate internal capabilities.