Data Analyst
Posted 4 Sep 2026
Advertised as “Intermediate Data Analyst”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3–5 yrs
- Management level
- Individual contributor
- Employment
- Hybrid
No salary listed for this role.
Field of studyComputer Science, Data Science, Mathematics, Statistics (or related)
Study levelBachelor's
We are X, bigly labs, Dis-Chem’s high-performance innovation hub; a place where bold ideas meet data, design, and radical customer focus to reimagine what healthcare can be.
Our mission is to power the future of healthcare by lowering costs, unlocking new possibilities, and improving access to healthcare for all South Africans. We do this through cutting-edge digital solutions that deliver smarter, more human, and truly patient-focused experiences. Here, your work is not limited to a whiteboard; it becomes real. It drives decisions, shapes experiences, and moves healthcare forward. We are driven by one big question: How do we use data and today’s technology to build breakthroughs to better health?
There’s only one question left: are you ready to make healthcare smarter, and actually mean it?
The Intermediate Data Analyst turns business questions into evidence: analysis in Databricks SQL and statistics on certified gold, delivered as findings and recommendations stakeholders act on. The deliverable is a decision, not a dashboard. Power BI carries the recurring answers as source-of-truth dashboards on certified gold and metric views, thin windows with no logic in DAX and no modelling in the tool.
WHAT WE'RE LOOKING FOR?
Minimum
- Bachelor's degree in Statistics, Mathematics, Data Science, Computer Science or a related field, or equivalent practical experience.
- 3 to 5 years in data analysis or business analytics.
- Strong SQL: joins, aggregations and window functions on large datasets.
- Working knowledge of statistics: distributions, hypothesis testing and significance.
- Data modelling literacy: reads and navigates star schemas and gold models, and mirrors them in Power BI semantic models without building logic in the tool.
- Power BI: builds dashboards with semantic-model wiring, DirectQuery on facts and Dual on dimensions, with clear layout and visual storytelling.
- Business understanding: frames analysis in the commercial mechanics of the domain, not just the data.
- Excel for working with data.
Advantageous
- Python or R for deeper statistical work.
- Databricks AI/BI dashboards.
- AI-assisted analysis: assistants for speed, every number verified against certified gold before it reaches a decision-maker.
- Report lifecycle and catalogue management.
- SAP business context: what BW reports mean and the data behind them. We teach this during the migration off SAP BW.
- Retail data, ideally pharmacy retail. Healthcare or financial services also count.