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monitoring frameworks to ensure data accuracy and consistency.\nTroubleshoot and resolve issues related to data quality, latency, or performance.\n\nCollaboration with Stakeholders: \nPartner with teams, analysts, and data scientists to understand data requirements and translate them into technical solutions.\nProvide technical support and guidance on data-related issues or projects.\n\nTooling and Automation: \nLeverage cloud-based solutions and frameworks (e. g., AWS) to streamline processes and enhance automation.\nMaintain and optimise existing workflows while continuously identifying opportunities for improvement.\n\nContinuous Learning and Innovation: \nStay current with industry trends, tools, and technologies in data engineering and marketing analytics.\nRecommend and implement innovative solutions to improve the scalability and efficiency of data systems.\n\nRequirements: \nBachelor's or master's in data science, Business Analytics, Information Science, Computer Science, Statistics, or a related field.\n2+ years of Experience as a Data Analyst with experience in Python (NumPy and Pandas), including (Matplotlib / Seaborn) and EDA, SQL and the scikit-learn library.\nExperience with AWS DB (S3 Bucket or Redshift) is required.\nBasic understanding of ETL / Glue data pipelines.\n\nBehavioural Competencies: \nAbility to work in collaborative environments and embrace diverse perspectives.\nCommunicate clearly and concisely, express thoughts and ideas effectively, and embrace cultural differences with respect when engaging with others.\nAbility to engage effectively with peers and stakeholders to build strong partnerships.\nPrioritise, maintain focus, and consistently deliver commitment.\nProactively understand customer expectations and willingness to create customer-based solutions.\n\nGood to Have: \nHands-on experience with dashboarding, e. g., QuickSight.\nDesired experience working with HR Data.\nExperience working with Glue/Lambda to develop data 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