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 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-cons