Senior Data Analyst

ROLE PURPOSE As Senior Data Analyst, you turn iiDENTIFii’s data into decisions the business 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 analysis role that sits close to both the business and the data platform. You are comfortable interrogating a messy dataset in the morning and presenting a customer engagement analysis to leadership in the afternoon. You bring analytical depth, sound judgement on data quality and governance, and the communication skill to translate complex findings into decisions non-technical stakeholders can act on.You 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. We care 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 we would like to hear from all of them.What Good 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 rebuilt by hand today 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 treat you as the person to ask about what the data actually says ROLE TASKS AND RESPONSIBILITIES Reporting, Dashboards & Self-Service Own end-to-end reporting for the 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 without going through you Own recurring client reporting end to end: definition, automation, quality assurance, delivery and the follow-up conversation — credible explaining a number, or a discrepancy, directly to a client’s team Handle ad-hoc requests with good judgement: establish the real question, deliver a clear and actionable answer, and prioritise sensibly when several arrive at once Choose the tool that fits the purpose across our broad set of reporting and visualisation technologies, matching the right one to the audience, the question and the shelf life of the answer Product & User Behaviour Analysis Analyse user behaviour across our 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 rather than on request Apply appropriate statistical rigour, including cohort analysis, significance testing, forecasting and seasonality, so that recommendations hold up when they are challenged Design and evaluate experiments with product, and be clear about what a result does and does not prove Investigate anomalies and unexpected patterns, and work out whether what you are seeing is genuine signal, a data quality issue, or noise Automation & Data Foundations Automate recurring analysis and reporting so that regular outputs run reliably to a schedule with quality checks, rather than being rebuilt by hand each cycle Build and maintain your own analytical models and reusable assets on top of the curated data, so knowledge compounds instead of living in one-off queries Specify what the analytics layer needs from upstream data (the sources, structures, definitions and refresh frequencies) and work with the engineering team to get there Understand the data platform well enough to be a genuine partner to engineering: where data comes from, how it flows, and what an upstream change will mean downstream Data Governance, Quality & Privacy Work responsibly with personal and sensitive data under POPIA, with client obligations that can extend further — least-privilege access, aggregation and de-identification as a matter of course Proactively identify and resolve data quality issues at the source, rather than patching around them downstream Apply sound data security, governance and metadata management practices across pipelines, dashboards and datasets Contribute to lean, practical data governance standards that balance rigour against the team’s pace of delivery Stakeholder Engagement, Mentorship & Documentation Partner with product, marketing, engineering and operations to ensure data is accessible, accurate and used effectively Communicate technical and analytical concepts clearly and confidently to both technical and non-technical audiences Represent the analytics perspective alongside engineers, architects and leaders on the broader data platform roadmap Mentor jun