12 Months Contract

Hybrid-Johannesburg

Role Overview

A Data Engineer designs, builds, and maintains scalable data pipelines and platforms that enable reliable data ingestion, storage, processing, and access. The role focuses on transforming raw data into high-quality, trusted datasets that support analytics, reporting, and data science use cases.

Key Responsibilities

• Design, develop, and maintain data pipelines (batch and/or streaming) Build and optimize data integration processes from multiple data sources Develop and manage data models for analytics and reporting
• Ensure data quality, accuracy, and reliability through validation and monitoring Implement and maintain ETL/ELT workflows
• Optimize data storage and query performance Collaborate with data analysts, data scientists, and business stakeholders
• Maintain documentation for data pipelines, schemas, and processes
• Enforce data governance, security, and compliance standards Troubleshoot and resolve data-related issues in production environments
• Support platform scalability, resilience, and cost optimization

Required Skills & Experience

• Technical Skills Strong proficiency in SQL Experience with Python, Scala, or Java
• Hands-on experience with data warehouses (e.g. Snowflake, BigQuery, Redshift, Synapse)
• Experience with ETL/ELT tools (e.g. Airflow, dbt, Azure Data Factory, Informatica) Knowledge of cloud platforms (Azure, AWS, or GCP)
• Understanding of data modeling (star schema, snowflake, dimensional modeling) Experience with version control (Git) Familiarity with CI/CD pipelines for data workloads Data & Platform Knowledge Relational and NoSQL databases File-based data formats (Parquet, Avro, JSON, CSV) Data streaming concepts (Kafka, Event Hubs, Kinesis – advantage) Performance tuning and query optimization

Soft Skills

Strong analytical and problem-solving abilities

Ability to work independently and in cross-functional teams

Clear communication with technical and non-technical stakeholders

Attention to details