Data Analyst

• Clean and organize large datasets for analysis and visualization using statistical methods
• verify and ensure accuracy, integrity, and consistency of data
• Identifying trends and patterns in data and using this information to drive business decisions
• Create the requirement artefacts e.g., Functional specification document, use cases, requirement traceability matrix, business test cases and process mapping documents, user stories for analytics projects
• Build highly impactful and intuitive dashboards that bring the underlying data to life through insights
• Generate ad-hoc analysis for leadership to deliver relevant, action-oriented, and innovative recommendations
• Improve data quality by using and improving tools to automatically detect issues
• Develop analytical solutions or dashboards using user-centric design techniques in alignment with ACT’s protocol
• Study industry/organization benchmarks and design/develop analytical solutions to monitor or improve business performance across retail, marketing, and other business areas
• Work with high-performing Functional Consultants, Data Engineers, and cross-functional teams to lead / support the complete lifecycle of visual analytical applications, from development of mock-ups and storyboards to complete production ready application
• Provide regular updates to stakeholders to simplify and clarify complex concepts, and communicate the output of work to business
• Create compelling documentation or artefacts that connects business to the solutions
• Coordinate internally to share key learning with other teams and lead to accelerated business performance

• Bachelor’s degree in computer science, Information Management or related technical fields
• 2 + years for Data Analyst
• Relevant working experience in a quantitative/applied analytics role
• Experience with programming and the ability to quickly pick up handling large data volumes with modern data processing tools, e.g. by using Spark / SQL / Python
• Working understanding of Statistical modelling using Analytical tools (Python, PySpark, R, etc.)
• Enterprise reporting systems, relational (MySQL, Microsoft SQL Server, etc.), and non-relational (MongoDB, DynamoDB) database management systems
• Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)
• Cloud computing services in Azure/AWS/GCP for analytics

Functional Analytics (Retail Analytics, Supply Chain Analytics, Marketing Analytics, Customer Analytics, etc.)