Background information about the job or company (e.g., role context, company overview)

We have been measurably improving the health of Kenyans since 1989. Our approach harnesses the vitality of the private sector to improve health outcomes for "Sara” - our archetype that focuses our interventions. We address the most serious health challenges affecting resource-poor and vulnerable communities in Kenya, including HIV/AIDS, reproductive healt...

Responsibilities or duties

Data Engineering: Support the design, build, and optimization of scalable data pipelines using cloud-based proprietary or open-source data platforms to deliver high-quality, analytics-ready datasets for enterprise reporting and advanced analytics. Implement best practices for data transformation and cleansing to ensure data integrity and reliability. Support the development and maintenance of technical documentation, including SOPs and user guides.

Machine Learning Operations (MLOps) Support: Enable and support machine learning workflows by developing feature pipelines, integrating models into production data environments, and contributing to automated model monitoring and processes.

Platform Operations & Reliability: Ensure the stability, performance, and reliability of data platforms through proactive monitoring, CI/CD practices, and production issue resolution.

Support the implementation of data analytics, visualization, and reporting tools.

Provide technical support in data quality management, data governance, security, and data privacy.

Embody PSI’s values: Measurement, Pragmatism, Honesty, Trust, Collaboration, and Commitment.

10–25% international travel, or flexibility to support global teams across regions.

This is not an exhaustive list of all tasks that an incumbent is expected to perform but is instead a summary of the primary responsibilities and requirements of the job. The incumbent may be asked to perform duties not included above. PSI reserves the right to revise job profiles at any time based on changes to the required job responsibilities.

Qualifications or requirements (e.g., education, skills)

Bachelor’s degree (or international equivalent) in Computer Science, Information Technology, Statistics, or a related field

Experience needed

At least 3 years of related experience; an equivalent combination of relevant education and experience may be considered

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

Strong data engineering skills, with experience building and maintaining ETL/ELT pipelines and working with both structured and unstructured data

Solid scripting skills in PySpark, Python, R, or SQL

Knowledge and appreciation of data modeling principles and database systems

Experience with Microsoft Fabric, Databricks, or similar cloud-based or open-source data platforms

Experience with data lake/lakehouse architectures and best practices for data storage and processing

Knowledge of machine learning concepts and frameworks

Strong problem-solving and critical thinking skills, with the ability to identify and mitigate risks and develop creative solutions

Experience using data analysis and visualization tools (e.g., Power BI, Superset, Tableau, D3.js)

Ability to build and maintain relationships with internal and external stakeholders; able to work both independently and as part of a team

Exceptional interpersonal and communication skills (verbal and written), with the ability to interpret data and present insights to non-technical audiences

• Support the design, build, and optimization of scalable data pipelines using cloud-based proprietary or open-source data platforms to deliver high-quality, analytics-ready datasets for enterprise reporting and advanced analytics.
• Implement best practices for data transformation and cleansing to ensure data integrity and reliability.
• Support the development and maintenance of technical documentation, including SOPs and user guides.
• Enable and support machine learning workflows by developing feature pipelines, integrating models into production data environments, and contributing to automated model monitoring and processes.
• Ensure the stability, performance, and reliability of data platforms through proactive monitoring, CI/CD practices, and production issue resolution.
• Support the implementation of data analytics, visualization, and reporting tools.
• Provide technical support in data quality management, data governance, security, and data privacy.
• Embody PSI’s values: Measurement, Pragmatism, Honesty, Trust, Collaboration, and Commitment.
• 10–25% international travel, or flexibility to support global teams across regions.

• Data Engineering
• ETL/ELT pipelines
• PySpark
• Python
• R
• SQL
• Data Modeling
• Database Systems
• Microsoft Fabric
• Databricks
• Data Lake/Lakehouse architectures
• Machine Learning Concepts
• Problem-solving
• Critical Thinking
• Data Analysis
• Data Visualization
• Power BI
• Superset
• Tableau
• D3.js
• Relationship Building
• Teamwork
• Interpersonal Skills
• Communication Skills (Verbal and Written)

• Bachelor’s degree (or international equivalent) in Computer Science, Information Technology, Statistics, or a related field