What will you do
- Translate customer pain-points into problem statements, develop analytics solutions, 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 source systems, 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
The ideal candidate should possess:
- Basic competency in two or more of the following areas preferred:
- Data visualisation 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
- 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
Additional experience good to have:
- Proficiency in AWS, Azure, Cloudera (or other cloud services), data pipeline and database development.
- 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
