Manager, Data Engineer, Data Innovation Office Job AKUH Nairobi, Kenya

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.

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

How to Apply

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