Data Engineer Job at Moringa – Business Intelligence and Data Engineering Role

Moringa is strengthening its Business Intelligence function and is looking for a Data Engineer to help build the infrastructure behind how the organisation uses information. The opportunity is aimed at an experienced data professional who can work across engineering, analytics, databases, reporting and stakeholder communication rather than focusing on only one part of the data process.

The position is a full-time, hands-on engineering role. The successful candidate will work closely with the BI Analyst and collaborate with leadership and department heads to turn business questions into dependable data products. This includes everything from taking raw information from internal systems and transforming it into useful datasets to building interactive dashboards that allow non-technical teams to understand information and make decisions with greater confidence.

The role is particularly suited to someone who enjoys taking ownership of technical problems from beginning to end. Instead of simply preparing datasets for someone else to use, the Data Engineer will have primary technical ownership of Moringa's internal BI platform and will contribute to the way data is collected, processed, modelled, secured and presented.

Building the Infrastructure Behind Better Decisions

The Data Engineer will work with information coming from several areas of the organisation, including admissions, marketing, operations, finance, Salesforce and other internal tools. Bringing these sources together in a reliable way is one of the central challenges of the position.

The successful candidate will design and maintain the pipelines responsible for moving information from source systems into the BI platform. This includes building ETL and ELT workflows, automating ingestion and transformation processes, and making sure information reaches its destination reliably.

Automation is particularly important because the role is expected to reduce the amount of manual reporting required by teams. A well-designed pipeline can also reduce the possibility of human error and create more consistent reporting processes.

But building a pipeline is only one part of the responsibility. The engineer will also need to monitor its health and respond when something goes wrong. This includes creating alerts for pipeline failures and data-quality problems, troubleshooting production issues and putting validation and testing checks into the data process.

The goal is not simply to move data from one place to another. The information arriving in the BI platform needs to be trustworthy enough for analysts, managers and operational teams to use.

Turning Data Into Something People Can Actually Use

The role becomes particularly interesting when engineering meets the user experience.

Moringa's internal BI platform uses a Python-based technology stack, including Django or Flask together with Plotly Dash for interactive dashboards and visualisations. The Data Engineer will help develop and maintain this platform while improving its performance and usability as the amount of data and number of users grow.

The dashboards are intended for people who may not have technical backgrounds. That means the engineer needs to think beyond whether the underlying code works. Information needs to be presented in a way that allows non-technical stakeholders to find answers to common questions without depending on an engineer for every request.

This creates a connection between software engineering, data engineering and business communication. A technically strong solution that users cannot understand or navigate effectively would not fully achieve the purpose of the BI platform.

The successful candidate will therefore be expected to build dashboards that are intuitive, responsive and visually clear while continuing to improve the architecture behind them.

There is also an operational side to maintaining the platform. The role includes deployment, versioning and basic DevOps practices such as environment configuration, CI/CD and containerisation.

The Database Has to Keep Up With the Business

As more information enters the BI environment, the underlying data architecture becomes increasingly important.

The Data Engineer will design, optimise and document data models that provide a reliable source of information for analysts, leadership and operational teams. This includes familiarity with approaches such as dimensional modelling, star schemas and snowflake schemas.

The purpose is to create a structure that remains understandable and useful as the organisation's data grows. Naming conventions, modelling standards and documentation practices will need to be maintained so that the data warehouse does not become increasingly difficult to manage over time.

Database design and query performance will also fall within the engineer's ownership. Dashboards and reports need to remain responsive as data volumes increase, which means the person in this role must understand how database structures and queries affect performance.

Strong SQL skills are consequently an important requirement. The successful candidate should be comfortable not only writing queries but also designing and optimising relational database schemas and the queries that operate against them.

Engineering Work Starts With Business Questions

Although the position is highly technical, it is not isolated from the rest of the organisation.

The Data Engineer will work closely with the BI Analyst and teams such as operations, finance and admissions to understand what information people actually need. Leadership and department heads will also be part of this interaction.

A business stakeholder may know the question they want answered without knowing what technical structure is required to answer it. The engineer's job is to translate that requirement into a scalable data product, dashboard or report.

This requires judgement. Stakeholders may have urgent requests that need a quick response, but a solution that is created only for immediate use can become difficult to maintain later. The Data Engineer is therefore expected to balance immediate business needs with the long-term health of the data infrastructure.

Communication is a major part of that process. Technical concepts and findings need to be explained in language that leadership and operational teams can understand.

Documentation is similarly important. The engineer will maintain information about data sources, definitions, transformation logic and dashboard usage so that knowledge does not remain with one person.

Reliability Includes Data Governance

Good data engineering is not only about speed and functionality. The information must also be managed responsibly.

The position involves championing data quality, consistency and governance across the organisation. Access controls and data security practices form part of this responsibility.

The Data Engineer will also be expected to identify opportunities to improve the existing infrastructure. This may include removing reports that are no longer useful or bringing together overlapping data sources.

The role therefore includes an element of continuous improvement. As tools, business requirements and data practices evolve, the engineer will need to identify where the existing approach can be strengthened and recommend improvements to Moringa's data stack.

Knowledge of data governance, security practices and privacy standards such as those associated with the ODPC is listed among the preferred skills.

Experience Matters More Than Simply Knowing the Tools

The position requires a bachelor's degree in Computer Science, Information Technology, Data or Software Engineering, Statistics or a related field, although equivalent practical experience can also be considered.

More importantly, candidates should have at least six years of professional experience in data engineering, analytics engineering or a closely related software or data role.

Python is a major requirement. Moringa is looking for someone with strong Python skills and experience building production-grade applications or services rather than someone whose experience is limited to scripts and notebooks.

Hands-on experience with Django and/or Flask is also required for building internal web applications or platforms. Plotly Dash experience is desirable for the dashboard side of the position, although someone with comparable experience and the ability to learn the framework quickly may also be considered.

The technical foundation continues with strong SQL skills, relational database design, ETL/ELT development, scheduling, orchestration, error handling and data modelling.

The candidate should also have experience working directly with non-technical stakeholders and delivering data products that people can actually use. Automated data testing and quality assurance experience is another important part of the required background.

Additional Technical Skills Can Strengthen the Profile

Several technologies and areas of experience are presented as useful additions rather than absolute requirements.

Experience with workflow orchestration platforms such as Airflow, Prefect or Dagster would be valuable. Familiarity with cloud platforms including AWS, GCP or Azure and cloud-hosted databases or data warehouses would also strengthen an applicant's technical profile.

The role can also benefit from experience with Git and collaborative software development practices such as code reviews and pull requests. Exposure to Docker and basic CI/CD pipelines is another advantage.

Knowledge of HTML, CSS and JavaScript could help with polishing internal dashboards and applications, while experience in education or working with admissions, operations or finance data would provide relevant domain familiarity.

There is also room for someone with mentoring experience, particularly if they have supported junior engineers or analysts. Familiarity with artificial intelligence and machine learning concepts is another useful addition, especially where data automation or predictive insights may be relevant.

The Person Behind the Engineering

Moringa's expectations extend beyond technical qualifications.

The organisation describes its working environment as a combination of an established education and technology organisation with the energy of a start-up. Its culture emphasises collaboration, customer focus, accountability and excellence.

Those principles connect closely with what is expected from the Data Engineer. The person in the role needs an ownership mindset and should be comfortable taking responsibility for a data product from its underlying pipeline through to the dashboard and communication with the people using it.

Attention to detail is equally important because reliable data depends on consistency, accuracy and proper documentation. At the same time, the engineer needs pragmatic problem-solving skills and must understand how to balance quick delivery with infrastructure that remains maintainable.

Adaptability is another important quality. Data tools and business requirements can change, so the successful candidate should be willing to keep learning and adjust their approach as the organisation develops.

A Role Where Engineering Meets the Whole Organisation

This Data Engineer opportunity is broader than a position focused purely on databases or pipeline development. It brings together software engineering, business intelligence, data architecture, reporting and communication.

The person hired will help determine how raw organisational information becomes something useful to the people making decisions. That responsibility runs through the entire process: data must be collected reliably, transformed correctly, modelled sensibly, protected appropriately and ultimately presented in a form that people can understand.

For an experienced data professional with at least six years in the field, strong Python and SQL capabilities, production engineering experience and an interest in building data products rather than isolated technical solutions, the position offers a substantial role within Moringa's BI function.

It is particularly suited to someone who enjoys seeing a technical project through to its practical outcome. The work begins with messy source data and complex business questions, but the intended destination is clear: reliable information that teams across the organisation can use with confidence.

Application

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