Data Engineer at Capitec Bank

• At Capitec, Data Engineers help build and optimise the pipelines, platforms and data systems that transform complex data into reliable, accessible insights. Our Data Engineering environment supports large-scale banking data, connects complex data sources to business needs, and focuses on secure, scalable and future-ready data solutions. We’re building a Data Engineer Talent Community for future opportunities across Data Engineer Levels. This is not for one specific vacancy — it’s a way for talented data engineers to connect with us early, learn about future opportunities, and be considered when roles aligned to your experience become available.You’ll be part of an environment where data engineering is more than maintaining pipelines. Our teams work across cloud-native data platforms, data lakes, data warehouses, batch and real-time data processing, scalable pipelines, data quality, governance and modern engineering practices. Senior-level roles also contribute to platform design, engineering frameworks, standards, technical mentorship and AI-enabled data engineering practices.

What you could work on
Depending on your level of experience, future Data Engineer opportunities may include:

• Designing, building, testing and maintaining ETL/ELT pipelines and data infrastructure that support analytics, reporting, product, risk and client experience decisions.
• Working with cloud-native data platforms, including AWS-focused environments, Redshift or similar cloud data warehouses, data lakes and data warehouses.
• Using Python, SQL, Git, CI/CD, Terraform or other Infrastructure as Code practices to build reliable, scalable and production-grade data solutions.
• Collaborating with Data Architects, Solutions Architects, Analysts, Product teams and technical stakeholders to translate business needs into practical data solutions
• Improving data quality, monitoring, governance, performance, reliability and scalability across the data ecosystem.
• Growing from foundational engineering delivery into independent pipeline ownership, solution design, technical leadership, mentorship and platform-level impact. 
• Technologies and practices you may work with
• Python | SQL | AWS | Redshift | Data Lakes | Data Warehouses | ETL/ELT | Batch Processing | Streaming / Real-time Pipelines | Git | CI/CD | Terraform / Infrastructure as Code | Data Modelling | Data Quality | Data Governance | Workflow Orchestration | Power BI / Microsoft Data Stack exposure | AI tooling and automation

Who should join this talent community?

• We're looking to connect with Data Engineers across a range of experience levels, from professionals building their foundational skills through to highly experienced engineers leading complex data initiatives. You may be a strong fit if you have experience with SQL, Python, data warehousing, cloud platforms (AWS, Azure or GCP), ETL/ELT pipelines, data modelling, and Agile delivery practices. Depending on your level of experience, you may be contributing to data pipeline development, system analysis and solution design, independently building and maintaining cloud-native data solutions, or taking technical ownership of large-scale platforms, architecture, performance optimisation, governance, CI/CD, infrastructure-as-code, and engineering standards. We welcome individuals who are passionate about data, enjoy solving complex problems, collaborate effectively with stakeholders, and are looking to grow their expertise in areas such as cloud engineering, data architecture, AI-enabled engineering, platform design, and technical leadership.

Why join our Data Engineer Talent Community?
This is an opportunity to connect with future roles in a data environment that offers:

• Enterprise-scale impact — work on data solutions that support banking products, decisions, risk, client experience and operational insight across a large retail banking environment
• A modern technical environment — grow your skills across Python, SQL, AWS, Redshift, cloud data warehouses, data lakes, CI/CD, Git, Terraform, orchestration, data quality and governance. 
• Growth across levels — progress from foundational engineering delivery to independent pipeline ownership, solution design, platform engineering, technical mentorship and staff-level contribution.
• Purposeful engineering — build reliable, accessible and future-ready platforms that help turn complex data into insight and innovation. 
• Continuous learning — role information and onboarding material reference learning pathways, AWS Cloud Practitioner Essentials, AWS DevOps Tools, AWS Glue and Terraform Associate learning topics for Product Data Engineers. 

Education (Minimum)

• Bachelor's Degree in Information Technology

Education (Ideal or Preferred)

• Honours Degree in Information Technology - Computer Science or Information Technology

Knowledge and Experience
Knowledge:

• Expert level SQL knowledge with query optimisation and tuning and indexing strategies.
• Architecture design for batch and streaming systems
• Deep knowledge of multiple programming languages and implementation of advanced algorithms for data processing.
• Design enterprise wide real-time / batch data integration architectures.
• Advanced knowledge of one cloud platforms data services.
• Design and implementation of data lake, data warehouse or other patterns.
• Implementation of enterprise-wide data governance frameworks with automated quality and monitoring.
• Ability to performance optimize across heterogeneous systems.
• Design of multi-environment GitOps strategies, that includes feature flags and other advanced techniques.
• Design & Implementation of containerized data solutions and infrastructure using infrastructure as code
• Design and configuration enterprise workflow orchestration tools and pipelines

Experience:

• Length of experience required is conditional on the qualifications obtained but must include:
• Proven track record of leading large-scale data engineering projects and driving business impact through data solutions 
• Experience with advanced data engineering tools and software (e.g., SQL, Python, Java, Apache Spark, Hadoop).
• Experience with machine learning algorithms and their application in predictive analytics 
• Experience in designing and implementing complex data architecture and infrastructure.
• With cloud computing platforms (e.g., AWS, Azure, Google Cloud) and their data services 2.
• Experience in aligning data engineering projects with business strategy and goals.
• Experience in providing data-driven recommendations to senior management and influencing decision-making.
• Experience in leading and mentoring less experienced engineers and data teams.
• In people development,  sharing and training.

Skills

• Analytical Skills
• Communications Skills
• Computer Literacy (MS Word, MS Excel, MS Outlook)
• Interpersonal & Relationship management Skills
• Problem solving skills