Data Engineer (FMCG / Food / Retail industry experience) at Merand Corbett & Associates – Bellville | Jobyssey

KEY RESPONSIBILITIES:
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.

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.

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.

The Data Engineer's primary responsibility is to build and maintain the data foundation rather than own business reporting or interpretation of business performance.

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.

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.

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.

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.

Artificial Intelligence & Agentic Capability

The 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 2.6.
Understanding of code harnesses and agentic coding tools, large language model behaviour, prompt engineering and retrieval-based approaches will be favourable.

KEY PERFORMANCE AREAS (KPAS):
CORE COMPETENCIES:

Analytical Thinking Able to understand complex data structures, identify relationships and systematically resolve data problems.
Problem Solving Approaches technical and data-related challenges logically and focuses on identifying sustainable solutions rather than temporary fixes.
Attention to Detail Maintains a high level of accuracy when working with large datasets, integrations and business-critical information.
Business Understanding Develops an understanding of how the business operates and ensures technical data solutions support practical business requirements.
Collaboration Works effectively across technical and non-technical teams and is able to translate business requirements into appropriate data solutions.
Ownership Takes accountability for the reliability, quality and performance of assigned data solutions.
Continuous Learning Keeps abreast of developments in data engineering, cloud technology, automation and analytics.
Communication Able to explain technical concepts clearly to stakeholders with different levels of technical knowledge.
Futuristic and Open Mindset
Ability to rapidly change and pivot

QUALIFICATIONS & EXPERIENCE:

A relevant tertiary qualification in one of the following or a related field: Computer Science / Information Systems / Information Technology / Data Engineering / Software Engineering
Relevant industry certifications in data engineering, cloud platforms or database technologies would be advantageous.

Experience

Approximately 5-7 years' relevant experience in data engineering, database development, systems integration or a similar technical data role. At least two of these years should include systems or data integration, or the exchange of data between internal and external systems.
Experience developing and maintaining ETL/ELT pipelines.
Proven experience in developing data projects using Python.
Strong practical experience working with SQL and relational databases.
Experience with data modelling and data warehouse concepts.
Experience working in a BI, analytics or reporting environment.
Experience designing, building or consuming API-based integrations (REST/JSON), including authentication, pagination, error handling, retry and idempotency behaviour.
Experience with file-based and batch integration to and from external parties, including scheduled transfers, secure file transfer (SFTP), and file validation, quarantine and reconciliation.
Experience publishing or consuming versioned, governed datasets or data products for downstream and third-party consumers, including schema change management and backward compatibility.
Experience delivering data or integration artefacts through source control (Git) and promoting them across development, test and production environments.
Experience monitoring and supporting production integrations, including failure detection, error triage, reprocessing or replay of failed records, and root-cause resolution.
Experience in a multi-site Retail, FMCG, Wholesale, Logistics or Supply Chain environment would be advantageous.
Experience supporting system implementations or business-transformation projects would be advantageous.
Experience with event- or message-driven integration (webhooks, publish/subscribe or streaming platforms such as Kafka), including at-least-once delivery, idempotency, replay and dead-letter handling, would be advantageous.
Experience with an integration or middleware runtime for example Apache Camel, Azure Integration Services, Boomi, MuleSoft or a comparable iPaaS/ESB would be advantageous.
Experience with master data management, data cataloguing or business glossary tooling would be advantageous.
Experience exchanging data with external trading partners, suppliers or service providers under defined data-sharing, security and privacy controls would be advantageous.

TECHNICAL SKILLS & KNOWLEDGE
Essential

Advanced SQL (MS, Postgres)
ETL/ELT development
Data modelling
Data validation and quality controls
Python/JavaScript
REST API design and consumption (JSON payloads, authentication, pagination, error handling, retries and idempotency)
DevOps or automated deployment practices including environment promotion across development, test and production
Microsoft 365 & Co-Pilot
Data integration patterns: batch, file-based, API-based and event-driven integration; incremental loads and change data capture; source-to-target reconciliation.
API and data-contract concepts: interface specifications (OpenAPI/Swagger), schema definition, versioning and backward compatibility.
Secure file transfer (SFTP) and structured file handling (CSV, fixed-width, JSON, XML).
Credential, key and secret management, and least-privilege access to data and integration endpoints.
Integration and pipeline observability: logging, monitoring, alerting, error handling, retry, dead-letter and replay.
Master data management, data cataloguing and metadata concepts, including business glossaries, data dictionaries and lineage.

Advantageous

Microsoft Foundry
Microsoft Fabric
Pascal Script
OData 4.01 and other standards-based data-access protocols
Containerisation and orchestration fundamentals (Docker, Kubernetes)
Distributed SQL query engines and lakehouse/object-storage concepts (for example Trino, S3-compatible storage, Parquet/Avro)
Observability tooling and standards (for example OpenTelemetry)
EDI and trading-partner exchange formats used in Retail, FMCG and Logistics
Ontology, taxonomy and semantic-layer modelling concepts

ONLY SHORTLISTED CANDIDATES WILL BE CONTACTED

Data Engineer (FMCG / Food / Retail industry experience)

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