Data & Analytics Engineer

About the Role

• The Data & Analytics Engineer owns Food4Education'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 metrics the organisation can rely on. The role ensures data arrives reliably, completely and securely; is modelled consistently so every report returns the same answer; and is documented to a standard that supports multi-country expansion and technical assistance to partner organizations. The post-holder works in a two-person engineering team in which each member leads one discipline and is fully capable in the other, working closely with engineering and platform teams, source-system owners, BI analysts and business teams.

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.

Minimum Requirements

Education:

• Bachelor's degree in Computer Science, Engineering, Statistics or a related field.

Experience:

• Minimum 4 years across data and analytics engineering, with production experience in both, including at least 2 years owning dbt and Big Query in production (models, tests and documentation) and demonstrated delivery of data migrations.

Skills & Competencies:

• Strong Python and SQL
• Advanced proficiency in BigQuery or an equivalent cloud data warehouse in production
• Dimensional modelling: grain, conformed dimensions, slowly changing dimensions and star schema design
• Production experience building and orchestrating data pipelines with tools such as Apache Airflow, Dagster, Cloud Composer or GitHub Actions, including scheduling, dependency management, retries and alerting
• Change data capture, incremental loading and backfill strategies across varied source systems
• Version control, code review and CI/CD applied to data work (GitHub or equivalent)
• Data protection controls and data migration procedures
• Able to translate a business metric definition into an implementable specification, and discuss it directly with non-technical stakeholders
• Attentive to data accuracy; documents as a matter of course and builds systems others can operate
• Raises problems early and is comfortable reporting known issues and quality gaps
• Communicates clearly with non-technical colleagues, system owners and vendors
• Organized and dependable under operational pressure

Certifications (if applicable):

• Cloud data platform or dbt certifications are an advantage but not required.

Preferred Qualifications

• ERP integration experience, Sage X3 or similar IoT or telematics data
• Experience supporting month-end financial close, ensuring finance data feeds are complete, reconciled and available on schedule
• Experience in a small team owning the full data stack

What Success Looks Like

Within the first 12 months:

• Data migrations and data integrations live within the expected timelines.
• New ingestions running on standard patterns rather than a bespoke build.
• Pipeline uptime above 90%, with alerts responded to within 4 hours.
• Failures caught by monitoring before a stakeholder reports them.
• Data and KPI dictionary covering more than 80% of core datasets.
• Personal data masked at source and access tiers in place across all reporting.
• Dependencies and governance based on business logics implemented on core datasets.
• Every incident closed with a documented root cause within 5 working days.