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Mids-Level Data Engineer
Job in
Cape Town, 7561, South Africa
Listed on 2026-09-17
Listing for:
SASSO CONSULTING
Full Time
position Listed on 2026-09-17
Job specializations:
-
Software Development
Data Engineering, SQL Developer, Python, AWS
Job Description & How to Apply Below
- 3―5 years' professional experience in Data Engineering, data-focused backend engineering or data architecture.
- Proven commercial experience actually building, maintaining and troubleshooting production ETL/ELT pipelines rather than purely academic/project exposure.
- Experience working with data warehouses, data architecture, data modelling and production data environments.
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems or related field, or equivalent practical experience.
- Strong/advanced Python for data engineering, ideally including Pandas and PySpark;
FastAPI exposure is also specified at mid-level. - Strong to expert SQL, including complex joins, window functions, debugging, query optimisation and query-plan/performance optimisation.
- Must be capable of troubleshooting and optimising both SQL queries and Python data jobs.
- Design, build, test, maintain and optimise scalable ETL/ELT pipelines.
- Batch processing experience, with real-time/streaming exposure highly valuable.
- Data ingestion from multiple sources including:
- REST APIs
- PostgreSQL/MySQL or other relational databases
- Third-party/SaaS platforms
- Structured, semi-structured and unstructured data
- Ideally Kafka/RabbitMQ or other message queues.
- Production pipeline monitoring, error handling, debugging, incident/root-cause resolution and preventative improvements.
- 3―5 years' professional experience in Data Engineering, data-focused backend engineering or data architecture.
- Proven commercial experience actually building, maintaining and troubleshooting production ETL/ELT pipelines rather than purely academic/project exposure.
- Experience working with data warehouses, data architecture, data modelling and production data environments.
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, Information Systems or related field, or equivalent practical experience.
- Strong/advanced Python for data engineering, ideally including Pandas and PySpark;
FastAPI exposure is also specified at mid-level. - Strong to expert SQL, including complex joins, window functions, debugging, query optimisation and query-plan/performance optimisation.
- Must be capable of troubleshooting and optimising both SQL queries and Python data jobs.
- Design, build, test, maintain and optimise scalable ETL/ELT pipelines.
- Batch processing experience, with real-time/streaming exposure highly valuable.
- Data ingestion from multiple sources including:
- REST APIs
- PostgreSQL/MySQL or other relational databases
- Third-party/SaaS platforms
- Structured, semi-structured and unstructured data
- Ideally Kafka/RabbitMQ or other message queues.
- Production pipeline monitoring, error handling, debugging, incident/root-cause resolution and preventative improvements.
- Hands-on experience with modern data warehouse/lakehouse platforms such as Snowflake, Big Query, Amazon Redshift or Databricks.
- Strong understanding of data models, schemas and dimensional modelling.
- Practical exposure to Kimball/star schema;
Data Vault is advantageous. - Understanding of storage/query optimisation including indexing, partitioning and compression.
- Solid working knowledge of at least one major cloud platform: AWS, Azure or GCP.
- Hands-on exposure to the platform's data services rather than simply having a cloud certification.
- The junior specification only required familiarity with cloud platforms such as Fabric, AWS or Big Query; the upgraded role requires solid working knowledge.
- Commercial experience with Apache Airflow, dbt, Prefect or a comparable orchestration/workflow…
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