Job Description

Role: BI Enablement Specialist with Data Warehousing solutions Experience

Location: Washington, DC (Ex Amtrak and Locals are highly preferred)

Duration: Long term

DT Applications Solution Architect Sr (BI Enablement Specialist) with Data Warehousing solutions exp

Key Responsibilities

• Design, develop, and maintain data, analytics & AI products to make insights accessible to the entire organization.
• The ability to work with cross-functional teams and clearly communicate complex data insights to non-technical audiences is essential.
• Design and deliver workshops, training courses, and educational materials to improve data, analytics, and AI literacy across the organization .
• Guide and mentor analysts and business stakeholders on data visualization best practices, analytics techniques, and the use of analytics tools.
• Work with various departments to understand their business requirements and develop tailored analytics solutions
• The ability to identify and solve problems related to data, processes, and tools to improve overall data utilization .
• Engage with teams to share data stories, promote collaboration, and create an integrated analytics-driven culture across the organization .
• Develop and maintain documentation, guidelines, and best practices on analytics processes, data visualization standards, AI tools, and their use
• Monitor the performance, usage, and business impact of Data, Analytics & AI initiatives to measure success .
• Keep up-to-date with the latest technologies and industry trends in data visualization and analytics to drive innovation.

Required Skills and Qualifications

• Experience in dimensional data modeling, software analysis, design, development , and testing for data warehousing solutions.
• Proficiency in data science, machine learning, or AI concepts is essential, along with practical experience in prompt engineering and using generative AI tools .
• Strong communication and presentation skills are critical to explain complex topics clearly to both technical and non-technical audiences
• A strong understanding of data quality, security, and governance principles is key.
• Proficiency in tools like Tableau, Power BI, or similar platforms is crucial for creating and interpreting data visualizations .
• Familiarity with SQL, and programming languages like Python or R, for data manipulation and analysis .

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