Data & Analytics Engineer

About the role

Food4Education is looking for a Data & Analytics Engineer in Nairobi who owns the organisation's data platform end to end: the pipelines and APIs that bring data from source systems into the data warehouse, and the models, tests and definitions that turn it into reliable metrics. The post-holder works in a two-person engineering team, each member leading one discipline and fully capable in the other, and works closely with engineering and platform teams, source-system owners, BI analysts and business teams.

Key responsibilities

• Data architecture: document and profile data sources (relational databases, Google Sheets, APIs, unstructured data) and design and maintain the unified BigQuery data model with naming, governance, partitioning, clustering and retention rules.
• Pipelines: build and maintain ETL pipelines with incremental loading, configure Apache Airflow (or equivalent) workflows, and deliver data migrations and new integrations for new countries.
• Metric modelling: design and maintain dbt models, implement core metric definitions, build dbt tests, maintain the KPI dictionary, reconcile figures and prepare certified self-service datasets.
• Data quality and protection: validation checks, quality dashboards, alerting and data lineage, incident logging and resolution, and masking or hashing of personal data at the ETL layer.
• Documentation and knowledge transfer: data dictionaries, runbooks, training of the BI team and ensuring systems can be operated without the post-holder present.

Required profile

• Bachelor's degree in Computer Science, Engineering, Statistics or a related field.
• Minimum 4 years of data and analytics engineering experience, including at least 2 years owning dbt and BigQuery in production and delivering data migrations.
• Preferred: ERP integration (Sage X3 or similar), IoT or telematics data, month-end financial close support, and experience in a small team owning the full data stack.

Required skills

• Strong Python and SQL; advanced BigQuery or equivalent cloud data warehouse.
• Dimensional modelling (star schema, conformed dimensions, slowly changing dimensions).
• Pipeline orchestration with Apache Airflow, Dagster, Cloud Composer or GitHub Actions.
• Change data capture, incremental loading, backfills, version control, code review and CI/CD for data.
• Ability to translate business metric definitions into specifications and communicate with non-technical stakeholders.