Manager, Data Engineer, Data Innovation Office

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

Stakeholder Engagement

Communicate project updates and milestones to stakeholders.

Solicit feedback from cross-functional teams to refine deliverables.

Resolve conflicts and manage stakeholder expectations.

Governance Compliance

Implement policies and procedures to ensure data security and privacy and ensuring compliance with data regulations.

Stakeholder satisfaction and seamless project execution through clear communication and alignment

Compliance with governance policies and country regulations ensures data integrity and mitigates risk

Conduct regular audits to verify compliance with governance standards.

Train team members on data governance requirements.

Relevant Experience and Qualifications

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or related technical field.

5+ years of data engineering experience, with at least 2 years in DevOps and cloud-native environments.

Strong technical aptitude and a love for working with data and using data to solve hard problems

Proven experience building and managing data platforms on AWS, Azure, or GCP.

Proficiency in Infrastructure-as-Code tools (e.g., Terraform, Pulumi).

Experience with CI/CD systems, container orchestration (e.g., Kubernetes), and operational monitoring.

Proven track record in building and shipping successful analytics software products at scale at a high-growth, high-tech company

Deep understanding of the different domains of Data Science: ETL, data analytics, machine learning, and operational research.

Strong track record of addressing the challenges of developing data products at scale.

Experience building out products that can meet the needs of a wide set of user personas ranging from simple to complex needs

Strong analytical and problem-solving abilities.

Excellent communicator and collaborator across multidisciplinary teams.

Entrepreneurial mindset with a proactive, get-things-done attitude.

Genuine excitement for solving complex problems and strong sense of empathy for the challenges faced by LMICs

Commitment to data security, governance, and operational excellence.

Strong references that speak to your ability to collaborate and communicate with stakeholders, designers, developers, data scientists, IT and researchers in an agile environment

To be a team player, coach, and referee all-in-one