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ABOUT THE ROLE
ALU is working to optimize its tech ecosystem and transition from fragmented data sources to a unified, scalable Business Intelligence platform. Reporting to the Senior Manager for Data, the Business Intelligence (BI) Lead will be a key contributor to this technical and operational transformation. This is a hands-on, systems-building role focused on execution rather than basic dashboarding. You will directly oversee the end-to-end implementation and daily operational stability of our data warehouse ecosystem and downstream presentation layers.
In this role, you will be the bridge between technical analytics infrastructure and day-to-day organizational operations. Working alongside platform engineers, data specialists, and data owners, you will be responsible for architecting the metrics frameworks, driving data cataloging, and partnering with engineering to define the robust underlying data pipelines necessary to democratize data across ALU. Your ultimate mission is operational: to establish a self-sustaining, self-service data environment that removes technical bottlenecks and empowers teams across the university with independent, reliable, and frictionless access to actionable data.
RESPONSIBILITIES
- Data warehouse architecture and BI delivery: Project manage and oversee the implementation and maintenance of the data warehouse ecosystem and high-impact, user-friendly dashboard suites (using viz platforms like PowerBI, Apache Superset, AWS Quicksight) tailored for departments across the organization.
- Data Lineage & Cataloging: Implement and maintain enterprise data catalogs (e.g., Atlan, Collibra, or open-source alternatives) to map data lineage from source systems to the final BI layer, ensuring complete transparency.
- Metric Standardization: Partner with cross-functional leadership to establish and maintain a centralized catalog of standardized corporate KPIs, ensuring consistent calculations across all institutional reports.
- Data Governance & Quality Liaison: Collaborate with the Platform Engineering team and other relevant stakeholders to ensure data models in our data warehouse accurately reflect complex business logic and business definitions.
- Data Quality Assurance: Implement automated data profiling and testing frameworks to catch upstream ingestion anomalies before they impact downstream BI deliverables.
- Stakeholder Enablement & Culture: Act as the primary champion for data literacy across ALU. Translate complex analytical concepts for non-technical teams and design "Train-the-Trainer" frameworks to maximize self-service data tool adoption.
- Responsible Use of AI: Leverage modern AI-assisted workflows to accelerate report generation, automate report documentation, and extract advanced semantic insights from structured data.
- Other duties as defined by the manager.
REQUIREMENTS
- BI & Analytics Systems Building: Proven track record of architecting and maintaining enterprise-grade data warehouse and analytics environments that drive operational agility and self-service independent data use.
- Hands-on Data Stack Experience: Practical, hands-on proficiency with cloud data warehousing concepts (AWS Redshift preferred), advanced SQL, and data transformation/modeling frameworks.
- Advanced Visualization Tooling: Expert-level mastery of visualization platforms (such as PowerBI, Apache Superset, AWS Quicksight) and strong capability in translating unstructured business requests into clean interface wireframes.
- Operational & Stakeholder Communication: Superior consultative and interpersonal skills. Ability to collaborate smoothly with cross-functional teams, interrogate ambiguous operational data needs, and translate complex technical architecture into accessible frameworks for non-technical users.
- Growth & Mission Mindset: Thrives in a highly dynamic, fast-paced setting; comfortable building robust frameworks within evolving systems rather than static corporate structures.
QUALIFICATIONS
- Education: Bachelor’s or Master’s degree in a quantitative field (e.g., Data Analytics, Computer Science, Statistics, Business Administration) or equivalent practical experience.
- Experience: Minimum of 5–7 years of experience in a business intelligence or data analytics function, with at least 2 years in a clear team leadership or project ownership role.
- SaaS Ecosystem Familiarity: Experience building BI layers directly on top of enterprise applications like Salesforce CRM/SIS, Oracle NetSuite ERP, and Canvas LMS is highly desirable.
- Localization Expertise: Familiarity with global and regional data privacy standards (e.g., GDPR or local Rwandan/Mauritian data protection laws) is a significant advantage.
