Senior data engineer
Senior data engineer owning the data pipeline layer — ingestion, transformations, validation, monitoring — for a retail investments technology team in Centurion, Pretoria. Works cloud/AWS-aligned with advanced Python and SQL, sets technical direction, and coaches junior engineers.
- Compensation
- Not disclosed
- Location
- Pretoria, South Africa
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- 7+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, Information Systems, Engineering or related field
- 4-7+ years in data engineering with production pipelines and platforms
- Advanced Python and SQL
- Data modelling, analytics-oriented schema design and warehousing
- Ingestion from relational databases, cloud storage, APIs and files
- Git, CI/CD and automation
- Agile or SAFe delivery
- Experience using AI in engineering work with ability to validate its output
- Able to explain constraints to business stakeholders and design choices to engineers
Nice to have
- Relevant certification
- Power BI or a similar BI tool
Full description
Pretoria, South Africa | Posted on 02/10/2026
The reporting is only as good as the pipelineunderneath it. This role owns that layer: the ingestion, the transformations,the checks, and the support when a number downstream does not match.
It is a permanent seat in Centurion, in a retailinvestments technology team, reporting to the Head of Application Development. You will build and run data pipelines and the platform they sit on, soanalytics, BI and the business get data they can trust. You will also settechnical direction in the team. That means design calls, engineeringstandards, and how AI is used in the work without breaking governance.
What you will do Deliver data engineering work inside an Agile team. Plan it,estimate it, break features into tasks, and hit the quality bar you agreedwith the product owner. Design and build ingestion and transformation pipelines acrosssystems and domains. Batch and incremental. Error handling, monitoring,alerting, validation and reconciliation included. Shape solutions that fit the target data platform. Theenvironment is cloud and AWS-aligned. You will be expected to call thecost, security, performance and support impact of a design before it isbuilt. Coach less experienced data engineers. Give analytics, BI anddata science a clear view of structures, availability and how a pipelineactually behaves. Review code and configuration. Simplify what is already there. Use Git, automated testing, CI/CD and structured releases. Document enoughthat someone else can support it. Use AI in the engineering workflow, on approved tools only. Youvalidate the output. You do not put client or proprietary data into apublic model. You stay accountable for what goes to production. Join incident response and root-cause work when a pipeline fails,and build privacy, access control and regulatory requirements in from thestart. Requirements Whatyou need Abachelor’s degree in Computer Science, Information Systems, Engineering ora related field. A relevant certification helps. 4–7+ yearsin data engineering, with production pipelines and platforms you can talkthrough. Advanced Python and SQL. Datamodelling, analytics-oriented schema design and warehousing. Ingestionfrom relational databases, cloud storage, APIs and files. Git, CI/CDand automation. Agile or SAFe delivery. You haveused AI in engineering work and you know how to check it. Power BIor a similar BI tool is useful. You canexplain a constraint to a business stakeholder and a design choice toanother engineer. #J-18808-Ljbffr