Senior Data Analyst

Senior Data Analyst

Cape Town

About Our Client

Our client is a fast-growing African technology company solving one of the most consequential problems in the region's digital economy, at scale and in high-stakes environments. Their platform is trusted by some of the continent's most demanding institutions precisely because it works. Their analytics function is small and growing, with strong foundations already in place: an expanding data warehouse, established governance and data quality practices, and reporting the business relies on. The gap now is capacity, not groundwork, and this role exists to close it.

The Role

As Senior Data Analyst, you will turn the business's data into decisions it can act on with confidence. Business questions rarely arrive fully formed, so your craft is to shape a broadly worded question into something that can genuinely be answered, answer it well, and land that answer with the people who need to act on it.

This is a senior, hands-on role that sits close to both the business and the data platform. You will be as comfortable interrogating a messy dataset in the morning as presenting a customer engagement analysis to leadership in the afternoon. You will work the lifecycle end to end: framing the question, modelling and interrogating the data, drawing out the insight, and seeing the recommendation through to a decision and its adoption.

Our client cares more about how well you think, how clearly you communicate and how curious you are about how data is built than about the particular tools you have used before. Strong analysts come from many backgrounds and industries, and they would like to hear from all of them.

What Success Looks Like in the First Six Months

• You own the core business and client reporting, and the definitions behind those numbers are documented, consistent and trusted across teams.

• Recurring reporting that is currently rebuilt by hand runs to a schedule, with quality checks attached.

• At least one piece of analysis you initiated, rather than were asked for, has changed a product, operational or commercial decision.

• Product, engineering and commercial teams see you as the person to ask about what the data actually says.

Key Responsibilities

Reporting, Dashboards and Self-Service

• Own end to end reporting for key business metrics, keeping definitions consistent and trusted across teams.

• Build and maintain dashboards and self-service tools that let teams find and act on insight independently.

• Own recurring client reporting from definition and automation through to quality assurance, delivery and follow-up, and be credible explaining a number or a discrepancy directly to a client's team.

• Handle ad hoc requests with sound judgement: establish the real question, deliver a clear and actionable answer, and prioritise sensibly when several arrive at once.

• Match the right reporting and visualisation tool to the audience, the question and the shelf life of the answer.

Product and User Behaviour Analysis

• Analyse user behaviour across products to identify trends, friction points and opportunities, and turn findings into concrete recommendations.

• Track and report on engagement, retention and customer journey metrics, flagging risks and opportunities proactively.

• Apply appropriate statistical rigour, including cohort analysis, significance testing, forecasting and seasonality, so that recommendations hold up under challenge.

• Design and evaluate experiments with the product team, being clear about what a result does and does not prove.

• Investigate anomalies and work out whether what you are seeing is genuine signal, a data quality issue or noise.

Automation and Data Foundations

• Automate recurring analysis and reporting so that regular outputs run reliably to a schedule with quality checks.

• Build and maintain analytical models and reusable assets on top of curated data, so that knowledge compounds rather than living in one-off queries.

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