Data Engineer

Working at Moringa 

• We are an established and respected part of Kenya’s tech and education ecosystem, yet we retain a fast-paced and dynamic culture that is reminiscent of a start-up. We are all mission-driven professionals with a passion for providing the best student experience possible. And we know that is only possible if our team is highly motivated. We value results, collaboration, and a customer-focused mindset, and offer a healthy dose of fun along with a hybrid working environment. 

Moringa Culture Code 

• Collaboration: We work together for a common goal 
• Customer Centric: The customer is at the heart of all we do 
• Accountability: I take ownership 
• Excellence: I deliver exceptionally 

Role Overview 

• Moringa is investing in a stronger Business Intelligence (BI) function, and this role is central to that 
• effort. We are looking for a Data Engineer to join our BI team and work closely with our BI Analyst to design, build, and scale the data infrastructure that powers decision-making across the organisation.
• This is a full-time, hands-on engineering role for someone who enjoys owning problems end to end. From raw, messy source data, through pipeline design and data modelling, all the way to a polished, interactive dashboard that a non-technical stakeholder can use with confidence. You will be the 
• primary technical owner of Moringa's internal BI platform, working in a Python-based stack (Django/Flask with Plotly Dash) to turn data from admissions, marketing, operations, and finance into reliable, actionable insight. 
• You will partner daily with the BI Analyst and regularly with leadership and department heads to 
• understand what questions the business is trying to answer and translate those questions into robust, scalable data products. 

Key Responsibilities 

Data Pipelines & ETL/ELT 

• Design, build, and maintain data pipelines and ETL/ELT workflows that reliably move data from source systems (admissions, finance, operations, Salesforce, and other internal tools) into the BI platform. 
• Automate data ingestion, transformation, and loading processes to reduce manual reporting effort and minimise the risk of human error. 
• Monitor pipeline health, build alerting for failures or data quality issues, and troubleshoot production issues quickly to minimise disruption to reporting. 
• Implement data validation, testing, and quality checks at each stage of the pipeline to ensure trustworthy outputs. 

BI Platform Development 

• Develop and maintain Moringa's internal BI platform using Python-based web frameworks (Django / Flask), with Plotly Dash for interactive dashboards and data visualisations, or other tools as deemed necessary. 
• Design intuitive, performant, and visually clear dashboards that allow non-technical stakeholders to self-serve answers to common questions. 
• Continuously improve the platform's architecture, performance, security, and user experience as usage and data volumes grow. 
• Manage deployment, versioning, and basic DevOps practices (e.g. environment configuration, CI/CD, containerisation) for the BI platform. 

Data Modelling & Architecture

• Build, optimise, and document data models (e.g. star/snowflake schemas, dimensional models) that provide a single, reliable source of truth for analysts, leadership, and operational teams. 
• Define and enforce data modelling standards, naming conventions, and documentation practices to keep the data warehouse maintainable as it scales. 
• Own the underlying database design and query performance, ensuring dashboards and reports remain fast and responsive as data grows. 

Stakeholder Collaboration & Reporting 

• Collaborate closely with the BI Analyst and stakeholders across operations, finance, admissions, and other departments to gather requirements and clarify business questions. 
• Translate business requirements into scalable, well-structured data products, dashboards, and reports, balancing stakeholder urgency and needs with long-term maintainability. 
• Present technical concepts and data findings in clear, non-technical language to leadership and operational teams. 
• Maintain clear documentation of data sources, definitions, transformation logic, and dashboard usage for internal knowledge-sharing. 

Data Governance & Best Practices 

• Champion data quality, consistency, and governance practices across the organisation, including access controls and data security best practices. 
• Identify opportunities to improve existing data infrastructure, retire redundant reports, and consolidate overlapping data sources. 
• Stay current with emerging tools and practices in data engineering and BI, and recommend improvements to Moringa's data stack where relevant. 

Required Qualifications and Experience 

• Bachelor's degree in Computer Science, Information Technology, Data/Software Engineering, Statistics, or a related field (or equivalent practical experience). 
• 6+ years of professional experience in data engineering, analytics engineering, or a closely related software/data role. 
• Strong proficiency in Python, including experience building production-grade applications or services (not just scripts/notebooks). 
• Hands-on experience with Django and/or Flask for building internal web applications or platforms. Experience in other programming languages is a plus. 
• Practical experience building dashboards or data visualisations with Plotly Dash (or a strong willingness and ability to ramp up quickly if experience is with a comparable framework).
• Experience working with other tools for creating dashboards and reports such as Power BI or Tableau is a plus. 
• Strong SQL skills and demonstrated experience designing and optimising relational database schemas and queries. 
• Proven experience designing and building ETL/ELT pipelines, including scheduling, orchestration, and error handling. 
• Solid understanding of data modelling concepts (dimensional modelling, normalisation, star/snowflake schemas). 
• Experience working directly with non-technical stakeholders to gather requirements and deliver usable data products. 
• Experience implementing automated data testing and quality assurance frameworks. 

Preferred / Nice to have Skills 

• Experience with workflow orchestration tools (e.g. Airflow, Prefect, Dagster, or similar). 
• Familiarity with cloud data platforms and services (e.g. AWS, GCP, or Azure) and cloud-hosted databases or data warehouses. 
• Experience with version control (Git) and collaborative development workflows (code review, pull requests). 
• Exposure to containerisation and deployment tools (Docker, basic CI/CD pipelines). • Experience in the education sector, or with admissions/operations/finance data specifically. 
• Familiarity with front-end basics (HTML/CSS/JavaScript) for polishing internal dashboards and applications. 
• Experience mentoring junior engineers or analysts. 
• Familiarity with AI and machine learning concepts or frameworks to leverage data automation and predictive insights 
• Knowledge of data governance, security best practices, and privacy standards (e.g., ODPC). 

Key Competencies 

• Ownership mindset. You must be comfortable owning a data product end-to-end, from pipeline to dashboard to stakeholder communication. 
• Strong communication skills. You should be able to translate technical detail into clear, actionable insight for non-technical audiences. 
• Attention to detail. We are rigorous about data accuracy, consistency, and documentation, and you should be too.
• Pragmatic problem-solving. You should know and understand how to balance speed of delivery with long-term maintainability of data infrastructure. 
• Collaborative. You need to be able to work well across many departments, with leadership, and other operational teams with differing levels of technical fluency. 
• Adaptability & Continuous Learning. Ability to stay current with new data engineering tools and adapt to evolving business requirements. 
• Documentation & Knowledge Sharing. Proven ability to document technical processes in a way that is accessible to the broader team, ensuring long-term scalability. 
• Data Product Mindset. A focus on viewing data pipelines as "products" that prioritize reliability, user experience, and continuous improvement for the end-user.