Data Analyst

Purpose of the Role:

The Data Analyst is Dis-Chem Life's rapid-response data explorer, tasked with moving quickly through diverse datasets to connect the dots, spot opportunities, flag risks, and surface patterns that help the business act in real time — not months down the line.

This role exists to ensure Dis-Chem Life makes fast, accurate, and informed decisions by transforming complex data into clear, actionable intelligence. By delivering reliable insights across policyholder, claims, operational, and market data, the Data Analyst helps improve performance, strengthen customer experience, and unlock growth opportunities.

Driven by curiosity, speed, and breadth, this role underpins strategic decisions and operational excellence, enabling the company to deliver on its promises and continuously innovate for our customers.

Role Summary:

To support the Data & Insights team by preparing, analysing, and interpreting data to deliver clear, accurate, and actionable insights that drive decision-making. The Data Analyst will work closely with senior analysts, data scientists, actuaries, and other business stakeholders to continually build and improve foundational analytics and insights capabilities.

Benefits:

• Competitive Salary
• Access to and freedom to explore the latest tools and technologies
• Opportunity to be part of a young growing team and to work with a confluence of insurance and retail data seldom available to most analysts.
• Ongoing professional development through structured learning and access to senior mentors.
• Be part of a company committed to nurturing talent and offering career advancement opportunities.
• The chance to influence the future of life insurance in South Africa and contribute to high-impact projects.
• Flexible working hours with hybrid options.
• Visionary Leadership

Key Responsibilities:

Data Preparation & Quality

• Collect, clean, validate, and organise datasets from multiple sources to ensure accuracy, reliability, and readiness for analysis.
• Identify data quality issues, anomalies, or inconsistencies and escalate or resolve them promptly to maintain data integrity.

Rapid Data Exploration & Analysis

• Investigate datasets quickly and effectively to uncover trends, outliers, patterns, and insights without overcomplicating the process.
• Compare metrics across time periods, geographies, product lines, or customer segments to identify actionable opportunities and risks.
• Perform descriptive and exploratory data analysis to support business decision-making.

Insight Generation & Hypothesis Support

• Distil complex data findings into simple, clear, and actionable insights tailored to business needs.
• Suggest hypotheses explaining observed trends and collaborate with senior team members to test and refine these ideas.

Reporting & Dashboard Support

• Create, maintain, and update reports and dashboards, ensuring timely, accurate, and easy-to-understand delivery using dashboarding tools.
• Develop quick, clear visualisations and summaries that address urgent business questions and support leadership decisions.

Ad-Hoc Analysis & Stakeholder Collaboration

• Respond to short-notice analytical requests with well-reasoned, actionable answers.
• Work closely with cross-functional teams (marketing, claims, operations, finance, etc.) to understand their data needs and ensure insights are relevant and timely.
• Assist in documenting data definitions, processes, and reporting standards to support consistency and transparency.

Continuous Learning & Development

• Proactively update skills and knowledge on new analytical tools, methodologies, and industry best practices.
• Seek opportunities to improve data analysis processes, reporting efficiency, and overall data literacy within the organisation.

Soft Skills:

• Naturally curious, you like to find out why something is happening.
• Fast but careful, you move quickly without sacrificing reliability.
• Strong communication skills, with the ability to present data-driven insights to both technical and non-technical teams in a clear and actionable way.
• Ability to approach problems with a data-driven mindset and offer actionable solutions that enhance business operations.
• Strong team player with the ability to work effectively across different departments to ensure data initiatives align with business goals.
• A sharp eye for detail in data accuracy, ensuring reports and dashboards are reliable and insightful.
• Managing multiple tasks and projects in a dynamic environment, while meeting deadlines and delivering quality work.
• Willingness to learn and adapt to the evolving needs of the business, with the ability to quickly pick up new tools and technologies.

 Technical Skills:

• Strong proficiency in writing basic to intermediate SQL queries for data extraction and manipulation
• Experience with advanced data wrangling e.g. cleaning, transformation, and preparation to ensure data accuracy and quality
• Familiarity with data visualization tools such as Apache Superset, Google Looker, Tableau etc to create clear and simple reports
• Basic understanding of statistical concepts including averages, percentages, variance, trend analysis, correlation, and simple forecasting
• Strong Microsoft Excel-like tabular data skills, including formulas, pivot tables, and data organization
• Knowledge of relational database concepts and database management principles
• Exposure to scripting languages like Python data analysis and automation (advantageous)
• Familiarity with automation tools such as Windmill to streamline data workflows (advantageous)

Experience:

• 2-3 years of experience working in data analytics, particularly in retail, healthcare, finance or insurance
• Hands-on experience writing SQL queries and working with relational databases.
• Exposure to data cleaning, preparation, and basic analysis in a business context.
• Experience creating reports and simple dashboards data visualization tools.
• Some familiarity with scripting languages (Python) and ETL processes (e.g. dbt) is a plus but not required at entry level.
• Experience collaborating with cross-functional teams to understand data needs and deliver insights.

 Qualifications:

• Sc./B.Com. degree in Big Data Analytics, Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Relevant certifications in data analysis, SQL, or visualization tools (e.g., Microsoft Certified: Data Analyst Associate, Tableau Desktop Specialist) are advantageous but not mandatory.