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
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