Data Analyst  

• Translate client management’s pain-points , develop analytics, and engagingly present results and learnings to both technical and non-technical audiences
• Contribute to the building and maintaining of end-to-end data pipelines to bring information from client, harmonise and cleanse data to support analytics solutions
• Contribute to scoping of data inputs, data cleaning and pre-processing, feature engineering, building analytics solution, deploying to production, conducting testing, and improving analytics solution by iterations
• Participate in technical design sessions with cross-functional teams to define data process flow, data definition, data & analytics solution requirements and specifications
• Work closely with project manager and technical leads to provide regular status reporting and support them to refine issues/problem statements and propose/evaluate relevant analytics solutions
• Work in interdisciplinary teams that combine technical, business and analytics competencies that deliver work in waterfall or agile software development lifecycle methodologies

What should you have:

• Degree in Computer Science, Computer Engineering, Engineering, Information Systems, Business Analytics, Mathematics, Statistics, Economics, Physics, or other related disciplines that possess an analytical and quantitative component from a reputable institution
• Fresh graduate or less than 2 years of relevant working experience
• Basic competency in two or more of the following areas preferred:
  • Data visualization tool (e.g. Tableau, Qlik, PowerBI)
  • SQL knowledge
  • Programming or scripting language (e.g. Python, R, Java)
  • Analytical software (e.g. SAS)
  • Distributed architectures (e.g. HDFS, Hive)
• Ability to analyse and break down complex concepts and technical findings into clear and simple language for communication to team members and clients
• Ability to link industry specific business requirements to BI, Analytics and Big Data solutions

Additional experience it would be great to have:

• Knowledge of statistical modelling, predictive analytics or machine learning
• Hands-on experience in setting up and using toolkits such as Docker, VMs, Git, SSH, REST APIs
• Certification in leading data analytics platforms such as Tableau, Qlik, Informatica, Talend, Microsoft, SAS, IBM