Data engineer
Econofoods · Bellville, South Africa ·
- Category
- Data engineering
Econofoods · Bellville, South Africa ·
Econofoods is a fast-growing FMCG Retail company specializing in frozen foods. Dedicated to delivering high-quality products at unbeatable prices, we prioritize customer satisfaction every single day. Our commitment to excellence, simplicity and our vibrant colourful people working at Econofoods sets us apart in the industry. Central to our identity is our unique HO HOLA Culture, characterized by appreciation and recognition. We celebrate the contribution of every individual and foster a supportive environment where everyone can thrive. We are currently seeking a dynamic individual to join our team and contribute to our ongoing success. If you are passionate about delivering exceptional customer service, collaborating with a diverse team, and embracing continuous learning and growth, Econofoods could be the perfect fit for you. Join us in our mission to provide quality products, value, and service to our customers, every single day.
PURPOSE OF THE ROLEThe Data Engineer is responsible for designing, developing, maintaining and improving the organisation's data infrastructure and data pipelines to ensure that accurate, reliable and accessible data is available for reporting, analytics and business decision-making.
The role supports the Business Transformation function by integrating data from multiple operational systems and creating scalable, automated data solutions that enable the organisation to better understand and improve its Retail, Logistics, B2 B Sales, Finance, Merchandising, Supply Chain and other business operations.
The Data Engineer works closely with Enterprise Architecture, Data Insights, IT, Business Process Management and functional business teams to ensure that data is effectively captured, integrated, structured, governed and made available for business use.
Key Responsibilities 1 Data Integration & Pipeline Development Design, develop and maintain reliable ETL/ELT data pipelines across multiple business systems. Integrate data from systems such as ERP, TMS, WMS, finance, CRM, supply chain, HR and other business applications. Reduce reliance on manual data extraction and manipulation through effective automation. Ensure data pipelines are scalable, efficient and appropriate for the organisation's future data requirements. Work closely with Data & Systems Architecture to ensure engineering solutions align with broader technology and architecture standards. 2 Data Quality, Integrity & Reliability Implement automated data-quality checks and validation controls. Assist with the implementation of MDM across the organisation. Identify and investigate inconsistencies, missing data, duplication and other data-quality issues. Work with system owners and business stakeholders to address root causes of data-quality problems. Monitor data pipelines and integration processes to identify failures or performance issues. Troubleshoot and resolve data-processing and integration errors. Ensure that data used for reporting and analysis is accurate, complete, consistent and available when required. Support the development of a trusted organisational data environment. 3 Analytics & Business Intelligence Enablement Develop and maintain analysis-ready datasets for Data Analysts, Analyst Developers and other authorised business users. Support the development of reliable data models for dashboards, management reporting and business intelligence solutions. Assist analysts with complex data extraction, transformation and integration requirements. Ensure that commonly used business information is sourced from controlled and consistent datasets. Enable greater self-service, democratized analytics through well-structured and governed data. 4 Automation & Continuous Improvement Review existing data flows and recommend improvements. Optimise database queries, pipelines and processing routines to improve performance. Proactively identify opportunities where improved data integration can simplify business processes. Contribute to continuous improvement initiatives within Business Transformation. Migrate data warehouse, data lake or similar data structures where applicable. 5 Business Transformation & Systems Projects Participate in business transformation, systems implementation and process-improvement projects. Assess data requirements when new systems, processes or technologies are introduced. Support data migration, cleansing, mapping and validation during system implementations. Work with Business Process Management to understand how information moves through business processes and identify opportunities for improved automation. Provide technical data expertise during solution design and project implementation. Support testing and implementation of new data solutions. Assist with post-implementation troubleshooting and optimisation. 6 Data Governance, Security & Compliance Apply organisational data governance standards across data engineering solutions. Ensure appropriate access controls are implemented for sensitive and confidential information. Work with IT and relevant stakeholders to ensure data solutions comply with information-security requirements such as POPIA and ISO27001. Maintain appropriate controls around the extraction, transfer, storage and use of organisational data. Support the establishment and maintenance of data stewardship, definitions and governance practices. 7 Documentation & Technical Support Maintain accurate technical documentation for data pipelines, integrations and data structures. Develop and maintain data-flow diagrams, integration specifications and data dictionaries where appropriate. Maintain appropriate change records for data solutions. Provide technical support and troubleshooting for data-related issues. Ensure that critical data processes are sufficiently documented to reduce dependency on individual knowledge. 8 Artificial Intelligence & Agentic CapabilityThe Data Engineer is expected to be a user, implementer and support resource for the organisation's artificial-intelligence and agent-assisted capabilities, applying them to data engineering, data quality and business-process outcomes within approved governance and security controls.
Use AI-assisted and agentic development tooling in day-to-day engineering work — pipeline and integration development, code generation and review, test creation, documentation and troubleshooting — and validate all generated code, SQL and configuration before it reaches a governed environment. Apply AI and machine-assisted techniques to business-process analysis, ETL/ELT development and data-quality optimisation, including anomaly detection, record matching and de-duplication, classification and rule suggestion, with human review before production use. Build and maintain the semantic and metadata foundations that make organisational data usable by AI and agent-based tools — business glossaries, data dictionaries, ontologies and taxonomies, and governed data-product contracts — so that natural-language questions return consistent, explainable and permission-aware answers. Support the implementation of AI-enabled platform capabilities together with Data & Systems Architecture and external technology partners, including configuration, integration, testing, user enablement and post-implementation optimisation. Apply AI within the organisation's data-protection and information-security requirements: confidential, personal or business-sensitive data may only be processed by approved AI services, consistent with the governance requirements in point 6. Understanding of code harnesses and agentic coding tools, large language model behaviour, prompt engineering and retrieval-based approaches will be favourable.