Data Engineer (Head Office, Durbanville) at STADIO Holdings

About the Role

• STADIO is seeking a Mid-Level Data Engineer to join our dynamic Continuous Improvement & Innovation team. In this role, you will play a key part in our continuous improvement lifecycle by developing the data infrastructure that powers data-driven decision making across the organisation. You will work closely with our Analytics Engineer to build and maintain robust data pipelines, a scalable data aggregation layer, and an efficient reporting environment. This is an exciting opportunity for a data engineering professional with a passion for large datasets and modern cloud technologies to make a real impact in a collaborative, forward-thinking environment.

Key Responsibilities

• Build and Maintain Data Pipelines:Design, develop, and manage robust data pipelines to ingest, transform, and load data from various sources into our data platform.
• Develop Data Aggregation Layer:Create and optimise a scalable data aggregation layer (data warehouse/data lakehouse) that consolidates large datasets and supports efficient querying and reporting.
• Collaborate on Data Solutions:Work closely with the Analytics Engineer and other stakeholders to understand data needs and ensure the data architecture supports analytical and reporting requirements.
• Ensure Data Quality and Performance:Implement data validation checks, monitoring, and alerting to ensure data accuracy, reliability, and optimal pipeline performance.
• Support Data-Driven Decision Making:Partner with analytics and business teams to provide the data foundations for dashboards, reports, and insights that drive strategic decision-making. Drive the implementation of a reporting tool from where staff can do self-service reporting.
• Continuous Improvement:Identify opportunities to improve existing data processes and contribute innovative ideas to enhance our data infrastructure as part of the continuous improvement lifecycle.

Qualifications & Skills Required

Education & Experience

• Bachelor’s degree in Engineering, Information Systems, Computer Science, or a related field.
• 4+ years of hands-on experience in data engineering or a similar role, with a track record of working on large-scale datasets and building data aggregation layers.

Technical Skills:

• Modern Data Stack Proficiency:Practical experience building data pipelines in a modern cloud environment. Familiarity with data lake architectures, SQL databases, and data integration tools on Azure (or similar platforms) is required.
• SQL & Programming Skills:Advanced SQL skills for data querying and transformation. Proficiency in at least one programming or scripting language (e.g., Python, .NET) for data processing.
• Data Modeling Knowledge:Solid understanding of data modeling techniques and designing scalable schema for analytics.
• Performance Tuning:Knowledge of optimising database and data pipeline performance (indexing, partitioning, caching strategies).
• Expertise in Microsoft Data Technologies:Strong experience with Microsoft’s data stack, especially Microsoft Fabric and its underlying components. Proficiency in tools such as Azure Data Factory (for ETL/ELT pipelines) and Azure Synapse Analytics (for data warehousing and big data processing) will be an advantage.
• Additional Cloud & Big Data Tools:Exposure to other cloud data services and tools (such as Azure Databricks, Azure Data Lake Storage, Power BI, or comparable tools on AWS/GCP) will be an advantage.
• Automation & Orchestration:Experience with workflow orchestration tools and CI/CD pipelines for data (e.g., Azure Data Factory pipelines, Git integration, DevOps for data processes).

Preferred Skills & Attributes:

• Analytical Mindset:Strong problem-solving skills and the ability to translate business requirements into efficient data solutions. Attention to detail in ensuring data accuracy and integrity.
• Communication & Teamwork:Excellent communication skills with the ability to work effectively in a collaborative team environment. Able to explain complex data concepts to non-technical stakeholders when needed.
• Agile Methodology:Comfortable working in Agile/Scrum teams and using tools for ticketing and collaboration (Azure DevOps, JIRA, etc.).
• Continuous Learner:Enthusiasm for staying up-to-date with emerging data technologies and best practices. A proactive attitude towards learning and continuous improvement will fit well with our culture.