Analytics Engineer - ShopriteX

eg. Accountant or Accounting or Kempinski



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Shoprite Group IT / Telecom Jobs in Cape Town


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The purpose of the Analytics Engineer role is to build and model data into robust, integrated and efficient data products to support and deliver best-in-class use case-led analytics across the organisation. The role works collaboratively within and across a multi-functional agile team, building data products and pipelines for high-impact projects that deliver scale and automation and improves data availability and quality.

The Analytics Engineer is a highly analytical and conceptual individual capable of engaging in the granular detail as well as the nuances of various systems to ensure data models are fully understood and built to deliver business value and drive data as a competitive advantage.

Role Description

  • Build data and feature marts for consumption by the data analyst, scientist, and machine learning communities
  • Set up monitoring, testing and automation procedures of data artefacts
  • Analyse business requirements and translate these into fit-for-purpose, robust and scalable data solutions


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  • Organise and transform data in a meaningful way and provide additional context as necessary so that it is ready for analysis
  • Execute testing procedures and routine monitoring of datasets
  • Maintain documentation related to datasets and analysis, ensuring that everyone on the data team uses the same language and definitions
  • Productionisation of data products
  • Provide first-line data pipeline support
  • Consult with data analysts and data scientists to refine scripts and procedures to improve and maintain standard guidelines for the data pipeline process
  • Explore and discover opportunities to improve systems, enterprises, and processes
  • Collaborate across teams to address and improve data quality at the source
  • Foster continuous improvement by coaching adoption of software engineering best practices
  • Provide data modelling expertise to various teams through code reviews and training

 

Qualifications and Experience

  • Degree or Diploma in a Computer Science, Engineering, Mathematics or a related field – (essential)
  • +2 years’ experience working as part of a data team, ideally as a Data Analyst or Data Engineer – (essential)
  • Advanced knowledge of SQL with the ability to write SQL that is easy to understand, support and maintain – (essential)
  • Proficiency working with large data sets and business models – (essential)
  • Exposure to and comfortable with adopting software engineering best practices including version control – (preferred)
  • Exposure to and at ease teaching others how to adopt best practices – (preferred)
  • Experience building and maintaining multi-functional relationships with various teams across the business – (essential)
  • Experience working in an Agile environment – (preferred)
  • Retail or eCommerce industry experience – (preferred)


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Key competencies and work ethic

  • Has strong analytical skills – Strong ability to collect, organise and assimilate disparate and multiple pieces of data to arrive at optimal solutions and embed the right structures and foundations
  • Strong technical aptitude with a passion and excitement for data, new technologies and solutions and its range of possibilities, applications and value for the business
  • High level of self-motivation and drive to set, meet and exceed goals and expectations.
  • Detailed, organised and quality-focused – Has an affinity for structure and efficiency and balances planning and execution
  • Good communication skills – Communicates well both verbally and in writing. Able to simply technical concepts and confidently convey information to a variety of stakeholders
  • Team player and collaborative partner - Builds sound working relationships across the business
  • Willing to coach/mentor others to build capacity and skills for data/analytics
  • Ability to work under pressure and under tight time constraints, efficiently prioritising workloads and managing their time effectively in a high-volume, fast-moving environment
  • Is curious and open to learning with a strong interest in data and discovery





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