Senior Data Analyst at Iidentifii

Job description

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 junior and intermediate analysts as the team grows, and contribute to internal training and knowledge sharing Maintain clear documentation of data models, dashboards and workflows, so the team is never dependent on one person’s memory AI-Augmented Ways of Working Use AI as a standard part of your workflow: exploring data, drafting and optimising SQL, building and iterating on reports, writing documentation, and pressure-testing your own analysis Build reusable AI workflows for your own work and share them with the team, so a repeatable analysis becomes a dependable automation rather than tribal knowledge Keep a critical eye: verify AI output, know where it goes wrong, and never present a number you could not derive and defend yourself Stay current with a fast-moving tooling landscape and bring what you find back to the team Living the iiDENTIFii Culture Champion iiDENTIFii’s culture and values in every interaction, and foster a team environment defined by intellectual rigour, psychological safety, collaborative excellence and a bias for action Actively contribute to iiDENTIFii’s mission: powering innovators in remote biometric digital authentication, globally QUALIFICATIONS, EXPERIENCE AND SKILLS NEEDED Experience 5+ years as a Data Analyst or in a similar analytical role, with the depth and reliability that come with it 5+ years of BI and data visualisation experience — we are deliberately tool-agnostic (Power BI, Looker, Tableau, Qlik or similar); what matters is that you have built things people actually use A track record of delivering analytical work independently in an agile, fast-paced environment: scoping, estimating, delivering, and seeing it through to stakeholder sign-off while managing competing priorities Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics or a related field, or equivalent demonstrable experience Exposure to a regulated or high-integrity environment (banking, insurance, telco, fintech, identity, risk or similar) is strongly advantageous — you will already understand why the numbers have to be right Technical & Analytical Skills Advanced SQL, and Python for analysis and automation Strong applied statistics for analytical work: cohort analysis, significance testing, forecasting and seasonality Experience with the Azure data stack (Data Factory, Synapse, Databricks) or an equivalent cloud data platform is valuable but not required Familiarity with CI/CD, version control and modern development practices applied to analytics is advantageous Experience with monitoring and observability tooling such as Application Insights, Azure Monitor or Grafana is a plus Governance & Domain Knowledge A working understanding of data security, governance and metadata management principles Comfort working responsibly with personal and sensitive data in a compliance-conscious environment Communication & Stakeholder Skills Strong communication skills, with the ability to translate insight into business impact for both technical and non-technical audiences Experience presenting your own analysis to senior stakeholders Experience presenting analysis directly to external clients is advantageous Ways of Working Comfortable owning work end to end, from framing a question through to seeing a recommendation adopted Experience supporting or mentoring less experienced analysts is a plus CULTURE AND BENEFITS Why Join Us iiDENTIFii is solving one of the most consequential technical problems in Africa’s digital economy: how do you verify that someone is who they say they are, remotely, at scale, across infrastructure that is inconsistent and high stakes? Our biometric identity platform is trusted by some of the continent’s most demanding institutions precisely because we have built something that works. The analytics function here is small and growing. The foundations are in place — an expanding warehouse, established governance and data quality practices, reporting the business relies on. The gap now is capacity, not groundwork. There is more high-value analysis waiting than we have people to do it, and this role exists to change that. Benefits Your money works harder here. As an iiDENTIFii employee, you gain exclusive access to preferential banking benefits, including competitive rates on home loans, vehicle finance, and personal loans—delivering real financial value from day one. Performance pays. Our competitive compensation package is designed to reward people who make an impact, because we believe exceptional work deserves exceptional recognition Family comes first. We offer enhanced family responsibility leave, giving you the time and space that matters most when life calls Your annual leave entitlement grows with your tenure Cape Town-based preferred, with a remote-first culture A technically excellent, mission-driven team at the frontier of biometric identity technology

Requirements

Must-haves

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
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

Nice-to-haves

Experience with monitoring and observability tooling such as Application Insights, Azure Monitor or Grafana is a plus
Experience supporting or mentoring less experienced analysts is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Effort: medium