Data Engineer - Johannesburg

HEINEKEN - the world's most international brewer. It is the leading developer and marketer of premium beer and cider brands. Led by the Heineken® brand, the Group has a portfolio of more than 300 international, regional, local and speciality beers and ciders. We are committed to innovation, long-term brand investment, disciplined sales execution and focused cost management. Through "Brewing a Better World"​, sustainability is embedded in the business and delivers value for all stakeholders. HEINEKEN has a well-balanced geographic footprint with leadership positions in both developed and developing markets. We employ over 80,000 employees and operate breweries, malteries, cider plants and other production facilities in more than 70 countries. Our most recent information is available on: and follow us on Twitter via @HEINEKENCorp. The Data Engineer is a key technical contributor within the Data & Analytics function, responsible for architecting, building, and operating the data infrastructure that underpins the organisation's analytics and reporting capabilities. With a minimum of three years' hands-on experience, the successful candidate will drive the end-to-end design of scalable data pipelines, govern data quality, and collaborate across business units to translate complex data requirements into robust, production-grade solutions. Key Responsibilities: Design and implement scalable, resilient data architectures aligned to enterprise strategy and business requirements. Build and maintain production-grade ELT/ETL pipelines that ingest, transform, and load data from diverse on-premises and cloud sources. Develop and version-control data models and schemas optimised for efficient querying, reporting, and ML consumption. Conduct performance tuning and optimisation of pipelines, queries, and storage layers to meet SLA and scalability targets. Design and maintain data integration solutions to ensure consistency and accuracy across systems. Implement data validation, cleansing, profiling, and enrichment frameworks as part of pipeline workflows. Enforce data quality KPIs; proactively monitor and alert on anomalies or pipeline failures. Support data lineage tracking and metadata management to enable full auditability. Build and manage cloud-native data solutions on Microsoft Azure Administer and optimise cloud storage and compute resources for cost and performance. Ensure all data engineering solutions adhere to data governance frameworks, privacy regulations and security policies. Implement role-based access controls, encryption at rest and in transit, and data masking where required. Contribute to the development and maintenance of data catalogues, data dictionaries, and governance documentation. Partner with data analysts, data scientists, and business stakeholders to gather requirements and deliver fit-for-purpose solutions. Produce and maintain comprehensive technical documentation — pipeline designs, runbooks, and architectural decision records. Qualification: Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field Relevant Master's degree or professional certifications (e.g. Azure Data Engineer Associate, Databricks Certified Associate) are advantageous Required Skills: 3+ years experience in data engineering or a related field Proficiency with analytical SQL engines Proficiency in Python for data engineering tasks (PySpark, pandas, data pipeline scripting) Hands-on experience with Azure cloud data services Experience with cloud storage Competency with data integration platforms Strong understanding of data warehousing concepts, ETL processes, and data modeling techniques. Working knowledge of big data technologies Experience with version control with Git and CI/CD tooling Familiarity with containerisation for data workloads Understanding of ML pipelines and MLOps practices to support data science teams Exceptional analytical and problem-solving abilities with a data-driven mindset Strong stakeholder management and collaborative working style Self-driven; able to operate independently in a fast-paced, ambiguous environment. Attention to detail and commitment to delivering high-quality, maintainable work. Highly beneficial skills: Experience with dbt for transformation layer management Knowledge of data observability and monitoring tools Infrastructure-as-Code experience with Terraform, Bicep, or ARM templates