Minimum 3 years building and maintaining production data pipelines that other teams relied on.
Undergraduate coursework, university and personal projects do not count toward this.
Certification required: a current cloud certification within the data, analytics, or machine learning track.
Certifications outside that track do not satisfy this requirement.
Snowflake SnowPro Core, production Snowflake experience, and machine learning deployed to production are each an advantage.
Advanced SQL: window functions, CTEs, set logic and aggregation at the correct grain, with the ability to read a query plan and explain a performance problem.
Relational database and modelling depth: normalisation, grain, key relationships, slowly changing dimensions, and judgement on when to denormalise.
dbt in a production setting: incremental models, snapshots, tests, and a project structure you designed rather than inherited.
Python for data work: analysis, automation, and acquiring data from databases, APIs, and files, including scraping where no interface is provided.
Data cleaning as an engineering discipline rather than a one-off task: profiling a source, reconciling it against a system of record, and holding it to that standard as it changes.
Quantitative and logical reasoning: statistics sufficient to validate a result, and the discipline to justify a design decision under questioning.
Presentation of data: turning a result into a clear chart, a written finding, and a recommendation that a non-technical audience can act on.
Excellent communication and presentation skills.
Self-Starter – Motivated – Customer-focused.
Where AI tools form part of how you work, you are expected to explain and defend every line of output you deliver. We regard them as an accelerator, not a substitute for understanding.
Employer closing dateNot provided
GoKazini listing ends21 Nov 2026
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