Data Analyst (Industrial Engineering NOT banking o

Essential skills: Python to create models Modelling Experience SQL - advanced DAX Power Query Data science course or qualification AI including ML Microsoft 365 (Advanced Excel) SAP PowerBI Experience in cloud platforms

REQUIREMENTS: Matric / Grade 12 or equivalent (Mathematics strongly recommended) Diploma or Degree in Data Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics or Industrial Engineering (or equivalent) Proficiency in SQL for data extraction and reporting (formal certification advantageous) Industrial Engineering Qualification Microsoft Power BI (DAX and Power Query / M), SQL (querying relational data sources), Issue-Based Information System (IBIS), SAP, Microsoft 365 (advanced Excel) and Microsoft Projects SQL Certification 4 to 6 years in a data analyst or business intelligence role, ideally within an operational, logistics, manufacturing or mining environment 3 to 5 years building reports and dashboards in Microsoft Power BI, including hands-on SQL for data preparation Exposure to business, asset or production data; experience with data modelling and data quality; and familiarity with Python for analytics is advantageous Exposure in the use of Generative AI and Machine Learning

RESPONSIBILITIES To extract and consolidate data from operational source systems (such as IBIS, SAP and other data sources) using tools such as SQL and Power Query To keep up to date with bleeding edge technology such as generative AI and build tools intelligently while keeping compliance to data governance and privacy protocols To deliver end-to-end reporting solutions, from problem definition and data acquisition through to data modelling, analysis and visualisation in Power BI To identify operational efficiency, utilisation and cost-optimisation opportunities across the business using data and statistical analysis To analyse and interpret operational data and translate findings into clear, actionable insights for decision-makers and through projects To develop and maintain the reporting datasets and Power BI dashboards by sourcing, structuring and validating data for accuracy, relevance and completeness, and integrating it into the data model within agreed deadlines To analyse fleet metrics such as but not limited to utilisation, productivity and associated data, identifying patterns and trends, and presenting insights and recommendations within agreed deadlines To implement and maintain data quality controls by profiling data, defining validation rules and quality measures, and monitoring data integrity on an ongoing basis To automate recurring and high-impact reports by standardising report definitions and building repeatable refresh and reporting structures, delivering within agreed deadlines To review flee

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