Key Responsibilities
1. Team Leadership & Development
• Recruit, lead, and develop the Business Analytics team
• Set clear goals, conduct performance reviews, and build individual development plans for each team member
• Create a culture of curiosity, data literacy, and continuous improvement within the team
• Mentor team members in coding, AI, and data engineering best practices
2. Analytics Strategy & Architecture
• Define and own the Business Analytics roadmap aligned with the NCC Strategy
• Design NCC’s end-to-end data architecture: ingestion, storage, transformation, and delivery
• Evaluate and adopt best-in-class tools and platforms for data warehousing, BI, and AI analytics
• Establish data governance standards: data quality, lineage, cataloging, and access control
3. AI & RAG System Development
• Lead the design and implementation of AI-powered analytics solutions using LLMs and RAG pipelines
• Build and maintain organizational knowledge bases using vector databases and embedding models
• Develop RAG-based Q&A systems that allow business users to query NCC data in natural language
• Integrate LLM APIs (OpenAI, Azure OpenAI, Anthropic Claude) into internal analytics workflows
• Define AI governance policies including responsible use, output validation, and privacy safeguards
4. Data Engineering & Coding
• Architect and oversee production-grade ETL/ELT pipelines using Python, SQL, and cloud platforms
• Review and approve code written by the team; enforce coding standards, testing, and documentation
• Build reusable data models, APIs, and automated reporting pipelines that scale with NCC’s growth
• Ensure all analytics systems are version-controlled, tested, and deployed through CI/CD workflows
5. Business Intelligence & Insights Delivery
• Own the development of executive dashboards and KPI frameworks across NCC’s departments
• Translate ambiguous business questions into structured analytical problems and clear deliverables
• Present data-driven insights and recommendations to senior leadership and department heads
• Partner with ITD Digital & Application and IT Operations teams to integrate analytics into products
6. Stakeholder Management
• Act as the primary analytics partner for department heads, finance, operations, HR, and marketing
• Facilitate data literacy workshops and training for non-technical business users across QSNCC
• Define SLAs for analytics requests and manage expectations with business stakeholders
• Represent Business Analytics in ITD steering committees and executive reviews
Qualifications:
Education
• Bachelor’s degree (required) in Computer Science, Data Science, Statistics, Information Technology, or a related field
• Master’s degree in Data Science, Artificial Intelligence, Business Analytics, or MBA (optional)
• Relevant certifications: Microsoft Azure Data Engineer, AWS ML Specialty, Google Professional Data Engineer, or equivalent (preferred)
Experience
• Minimum 3-5 years of hands-on experience in data analytics, data engineering, or a related field
• Minimum 1 years in a team lead, senior, or management role overseeing analytical or technical staff
• Demonstrated experience building and deploying AI/ML or LLM-based solutions in a production environment
• Proven track record designing and operating RAG pipelines with vector databases at scale
• Experience working in hospitality, venue management, events is advantageous
Coding & Engineering
• Advanced Python programming: pandas, numpy, FastAPI, asyncio, pytest — confirmed through portfolio or technical assessment
• Advanced SQL across multiple dialects (MySQL, PostgreSQL, MSSQL) including query optimization and database design
• Experience with cloud data platforms: Azure Synapse, Databricks, AWS Redshift, or Google BigQuery
• Working knowledge of JavaScript or TypeScript for API and lightweight front-end data integrations
• Strong Git discipline: branching, pull requests, code reviews, and CI/CD pipelines for data products
AI & Machine Learning
• Hands-on experience integrating LLM APIs into business workflows (OpenAI, Azure OpenAI, Gemini, Anthropic Claude)
• Experience with machine learning model development: classification, regression, forecasting using scikit-learn or similar
• Prompt engineering expertise: designing, testing, and iterating prompts for consistent and reliable outputs
• Familiarity with model evaluation metrics, A/B testing for AI features, and production model monitoring
RAG & Knowledge Systems (Required)
• Demonstrated experience building Retrieval-Augmented Generation (RAG) pipelines end-to-end
• Practical knowledge of vector databases: Pinecone, Weaviate, ChromaDB, Qdrant, or pgvector
• Experience with embedding models (OpenAI Ada, HuggingFace Sentence Transformers) and similarity search
• Proficiency with orchestration frameworks: LangChain, LlamaIndex, or equivalent
• Ability to design chunking strategies, document ingestion pipelines, and retrieval evaluation using RAGAS or similar frameworks
• Understanding of hybrid search (dense + sparse retrieval), re-ranking, and context window management
Responsibilities:
Qualifications:
Responsibilities:
Qualifications:
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