Position Summary

The Aga Khan University (AKU) possesses a rich, diverse, and growing repository of research and operational data. To further unlock the potential of these data assets, the Manager, Data Engineer will design, build, and manage advanced data environments to enable research excellence. Data engineers are the bloodline of any data product; they transform raw data into useful and impactful insights. This role requires strong analytical skills and the ability to combine data from different sources. The incumbent will be proficient in building data pipelines and data repositories that are optimised for scale and performance. This role focuses not only on building scalable and secure research data platforms but also on managing them operationally through DevOps practices, ensuring performance, reliability, security, and compliance. The Manager, Data Engineer will work collaboratively with researchers, data scientists, and cross-functional teams to create fit-for-purpose data solutions that facilitate high-impact academic research and social innovation.

Key Responsibilities

Team Leadership

• Guide multidisciplinary teams to align on project goals, timelines, and technical approaches.
• Facilitate collaborative problem-solving sessions.
• Mentor junior engineers and foster a culture of innovation and continuous learning.

Technical Ownership

• Review and approve project designs, ensuring adherence to best practices.
• Monitor project progress and resolve technical challenges.
• Implement risk mitigation strategies to meet deadlines.

Environment and Platform Architecture

• Design scalable, secure research data environments
• Develop scalable ETL (Extract, Transform, Load) processes to support data movement.
• Automate data workflows for real-time and batch processing.
• Ensure data pipelines are optimized for performance and cost-efficiency.

DevOps and Infrastructure Management

• Build Infrastructure as Code (IaC) for deployments.
• Implement CI/CD pipelines for data platforms.
• Automate monitoring, scaling, and disaster recovery.
• Manage upgrades, patching, backups, and incidents.

Platform and Repository Design

• Assess project requirements to determine the appropriate architecture.
• Design and implement storage solutions, such as data lakes and warehouses.
• Integrate data platforms with existing infrastructure.

Data Optimization

• Extract and preprocess data from operational systems for analytical use.
• Optimize data structures for speed and usability in analytics and reporting.
• Create metadata documentation to enhance usability.

Model Development

• Develop logical and physical data models based on business and research needs.
• Implement models to support operational dashboards and reporting systems.
• Validate models for performance and scalability.

Advanced Data Preparation

• Cleanse and transform data to prepare for machine learning models.
• Apply feature engineering techniques to improve model performance.
• Ensure data is securely stored and accessed during modeling processes.

Algorithm and Prototype Development

• Design algorithms to solve specific research or operational challenges.
• Build prototypes to validate hypotheses or test new ideas.
• Optimize algorithms for scalability and efficiency.

Data Quality and Reliability

• Establish automated data quality monitoring mechanisms.
• Develop and implement data validation rules.
• Address data anomalies and implement corrective measures.

Collaboration

• Host regular meetings with data scientists, report developers, and researchers to align on requirements.
• Translate business needs into technical specifications.
• Provide feedback on how data can support organizational goals

• Guide multidisciplinary teams to align on project goals, timelines, and technical approaches.
• Facilitate collaborative problem-solving sessions.
• Mentor junior engineers and foster a culture of innovation and continuous learning.
• Review and approve project designs, ensuring adherence to best practices.
• Monitor project progress and resolve technical challenges.
• Implement risk mitigation strategies to meet deadlines.
• Design scalable, secure research data environments
• Develop scalable ETL (Extract, Transform, Load) processes to support data movement.
• Automate data workflows for real-time and batch processing.
• Ensure data pipelines are optimized for performance and cost-efficiency.
• Build Infrastructure as Code (IaC) for deployments.
• Implement CI/CD pipelines for data platforms.
• Automate monitoring, scaling, and disaster recovery.
• Manage upgrades, patching, backups, and incidents.
• Assess project requirements to determine the appropriate architecture.
• Design and implement storage solutions, such as data lakes and warehouses.
• Integrate data platforms with existing infrastructure.
• Extract and preprocess data from operational systems for analytical use.
• Optimize data structures for speed and usability in analytics and reporting.
• Create metadata documentation to enhance usability.
• Develop logical and physical data models based on business and research needs.
• Implement models to support operational dashboards and reporting systems.
• Validate models for performance and scalability.
• Cleanse and transform data to prepare for machine learning models.
• Apply feature engineering techniques to improve model performance.
• Ensure data is securely stored and accessed during modeling processes.
• Design algorithms to solve specific research or operational challenges.
• Build prototypes to validate hypotheses or test new ideas.
• Optimize algorithms for scalability and efficiency.
• Establish automated data quality monitoring mechanisms.
• Develop and implement data validation rules.
• Address data anomalies and implement corrective measures.
• Host regular meetings with data scientists, report developers, and researchers to align on requirements.
• Translate business needs into technical specifications.
• Provide feedback on how data can support organizational goals

• Data Engineering
• ETL (Extract, Transform, Load)
• DevOps
• Infrastructure as Code (IaC)
• CI/CD pipelines
• Data Lakes
• Data Warehouses
• Data Modeling
• Machine Learning
• Feature Engineering
• Algorithm Design
• Data Quality Monitoring
• Data Validation
• Collaboration
• Team Leadership
• Problem-solving
• Risk Mitigation
• Performance Optimization
• Cost-efficiency
• Scalability
• Security
• Reliability
• Compliance
• Disaster Recovery
• Monitoring
• Patching
• Backups
• Incident Management
• Metadata Documentation
• Analytical Skills
• Technical Specifications

• BA/BSc/HND
• MBA/MSc/MA
• 5 years of experience