AI & Business Intelligence Engineering Manager

Job Description

Job Summary

The AI & Business Intelligence Engineering Manager will lead the design, development, and integration of AI solutions while performing data analysis, forecasting, and business strategy initiatives. This hands-on role involves full-stack software development using technologies such as React, Python, JavaScript, and large language models (LLMs) and other AI technologies to build an AI career coach application. The candidate will also be responsible for automating processes including ETL of data and report writing - through programming and AI, and within cloud computing environments. A solid understanding of higher education is essential, along with excellent communication and collaboration skills.

Key Responsibilities & Accountabilities:

AI Development & Full-Stack Engineering (30%)

• Lead and execute the design, development, and integration of AI and Machine learning technologies and applications using React, Python, JavaScript, and LLMs and ML/DS libraries.
• Build and maintain an AI career coach application with seamless cloud integration (Azure, CI/CD pipeline) for CPE.
• Provide technical leadership and collaborate with research and technical teams to optimize and troubleshoot AI-driven solutions.

Data Management, Analysis & Forecasting (30%)

• Perform and oversee data pre-processing, analysis, and forecasting initiatives using Python and related technologies like Jupyter Notebook.
• Develop and monitor key performance indicators (KPIs) that drive program improvement and strategic planning for CPE senior leadership.
• Automate ETL processes and report writing using programming and AI to ensure accurate and timely data flow.
• Deliver clear, actionable reports and visualizations that support data-driven decision-making for CPE stakeholders

Business Intelligence & Strategic Collaboration (20%)

• Collaborate with leadership, business teams, and prospective partners to align data intelligence with strategic goals, business requirements, and partnership objectives.
• Develop and deliver engaging, data-driven presentations, custom reports, and actionable recommendations that highlight the value of CPE's programs and partnerships.
• Serve as the technical liaison between other university units and CPE leadership, ensuring that data insights are accurately collected and translated into practical, actionable strategies.
• Provide hands-on support by troubleshooting reporting system issues and working with CPE stakeholders to tailor and optimize data solutions.

Data Infrastructure Development & Maintenance (20%)

• Design and implement robust data architectures and infrastructures to support analytics and reporting needs.
• Ensure data quality and consistency, collaborating with IT and cross-functional teams to resolve discrepancies.
• Develop and maintain tools and processes that streamline data collection, storage, and analysis.

Required Skills and Qualifications:

• Programming & Full-Stack Development:
  • Proficiency in Python, JavaScript, and modern frameworks like React and Flask.
  • Experience with RESTful API development and cloud integration.
  • Experiencing building software and doing research in the education space.
• Generative Artificial Intelligence & LLMs:
  • Hands-on experience with generative AI models (e.g., OpenAI GPT, Llama, Gemini) and familiarity with prompt engineering and Retrieval Augmented Generation.
• ETL Automation & Data Engineering:
  • Expertise in automating ETL processes and report generation using programming and AI.
  • Knowledge in data pre-processing, analysis, and forecasting.
• Cloud Technologies & DevOps:
  • Experience working with cloud platforms (e.g., Azure, AWS, Google Cloud), preferably Azure.
  • Familiarity with containerization (Docker) and CI/CD pipelines.
• Data Visualization & Business Intelligence:
  • Proficiency with BI and visualization tools such as Power BI or Tableau or other data science libraries/frameworks.
  • Strong analytical skills to transform data into actionable insights.
• Software Engineering Best Practices:
  • In-depth knowledge of version control systems (e.g., Git), agile methodologies, and testing frameworks.
  • Commitment to writing clean, maintainable, and scalable code.
• Communication & Collaboration:
  • Excellent communication and interpersonal skills for effective cross-functional teamwork.
  • Ability to translate complex technical concepts into clear, business-relevant strategies.