Data & Analytics Engineer at Food For Education

Key responsibilities

• Data architecture and source assessment: Document and profile all data sources — relational databases, Google Sheets, APIs and unstructured data — for quality, volume, update frequency, key relationships and business entity mappings. Design and maintain the unified BigQuery data model, applying agreed naming and governance standards, partitioning and clustering for cost, and retention and historisation rules, designed for transfer across countries and partners.
• Pipeline development and orchestration: Build and maintain extraction, transformation and enrichment pipelines for every source system, using incremental loading to minimise processing cost, and deliver data migrations, new integrations and ingestion for new countries. Configure and maintain Apache Airflow (or equivalent) workflows with business-aligned scheduling, error handling, retries and backfills, and monitor pipeline health to resolve failures.
• Analytics engineering and metric modelling: Design and maintain dbt models from staging to serving layers with consistent grain, naming and conformed dimensions. Implement core metric definitions so every report, dashboard and external submission uses the same logic; build dbt tests across critical assets; maintain the KPI dictionary (definition, logic, source, refresh cadence, as-at date, businessowner); reconcile figures where systems disagree; prepare certified self-service datasets; and flag definitions that cannot be implemented as written, working with system and business owners to close gaps at source.
• Data quality, protection and incident management: Implement validation checks at extraction and loading. Maintain quality monitoring dashboards, data dictionaries, alerting tools, and data lineage, while logging, triaging, resolving, and documenting incidents against agreed severity levels. Mask or hash personal data at the ETL layer, implement row- and column-level access control across agreed tiers, ensure no model reintroduces personal data, and support access reviews and data protection requirements.
• Documentation and knowledge transfer: Maintain the raw-layer data dictionary, KPI dictionary, dependencies and lineage; document pipelines, transformations, models and operational runbooks; train and support the BI team on data models and access patterns; and ensure systems can be operated and transferred without the post-holder present.

Candidate requirements

• Location Nairobi
• Job Field Data, Business Analysis and AI &nbsp, ICT / Computer &nbsp.

Required skills