Data Analyst I

**About the Team**

ShopriteX was established to accelerate the use of data to improve customer experience and business operations. Our Agile way of working allows for flexibility to grasp new opportunities and quickly transform them into insights that will have a direct impact on strategic initiatives.

The Insights & Data Monetisation Tribe harnesses big data, technology and advanced analytics to derive revenue from customer insights. It is made up of three squads: Assortment & Customer Intelligence, Pricing & Promotions and Supplier Insights. Some highlights of the Tribe duties include developing customer centric analytics products and insights, building recommendation models to ensure we have the correct product offering, evaluating the end-to-end effectiveness of promotions to reduce sales dilutive promotions and identifying key value items driving brand price perception to maintain price leadership.

As a data analyst you must be curious, enjoy digging into data to unearth insights and be passionate about the value this can add. Willing to take initiative, expand on new responsibilities and empower yourself with new skills and techniques as you grow with the team. This is an exciting opportunity and will be a close and collaborative journey with the IDM Tribe to develop your technical skills and grow into an advanced analytics role.

**Role Purpose**

The purpose of the Associate Data Analyst role is to leverage Shoprite's rich customer and transactional retail data assets to provide actionable trends and insights that enable business to make sound commercial decisions. The role works collaboratively in an agile team, modelling and framing well-defined business scenarios to solve analytical problems, performing experimental design approaches to validate findings or test hypotheses, depicting the rationale of findings in easy-to-understand terms for business and providing consolidated business reports and metrics. The Associate Data Analyst has a passion for developing their skills in the art of data analytics, requiring a combination of technical, analytical and communication skills to infer meaning from data and translate these in a concise, cohesive story.

**Role Description**
- Under the guidance of the Lead / Senior Data Analyst, work with business stakeholders to understand and correctly frame analysis requirements, hypotheses and questions and propose fit-for-purpose data led solutions.
- Prepare data sets, ensuring the right data is collected and the right analytical techniques are applied to produce the right business insights.
- Write standard business requirements/modifications to transform data into common and graphical formats and reports.
- Build and publish comprehensive dashboards, reports and analytical findings that enable business to draw meaningful insights.
- Support with data related queries from business stakeholders, helping to resolve issues or enable understanding and integration of findings to unlock key insights.
- Validate datasets and reports and proactively resolve any data or metric related issues.
- Evaluate the accuracy of data sources and data gathering techniques and ensure data quality and integrity is maintained.
- Maintain documentation, user guides or training material, ensuring the same language and definitions are applied.
- Share insights across teams to inform collaborative efforts.
- Stay updated on developments in data analytics and adjacent fields to ensure the latest techniques and methods are applied.

**Qualifications and experience**

Degree or Diploma in Operations Research, Statistics, Engineering, Information Technology, Information Systems or a related field - (essential).

**Essential**:

- Strong proficiency in MS Word with well-developed Excel skills - (essential).

**Desired**:

- Exposure to creating dashboards and reports using data visualisation tools (e.g., Tableau, Power BI)
- Foundational knowledge of SQL and relational databases
- Foundational knowledge of AWS cloud computing based solutions (Python / PySpark)
- Exposure to an agile environment
- Exposure to retailer specific data (e.g. brand, customer, shopper, category, sales, promotions, pricing, channel, POS, payment / tender, store, stock and supply chain data)