[What the role is]
This is a 2-year contract position as a Lecturer in the School of InfoComm Technology.
You will be involved in full-time and part-time course offerings.
[What you will be working on]
Your responsibilities will include, but are not limited to, the following:
- Lead in design, development and review curriculum, facilitate student learning, supervise projects, conduct assessments
- Participate in and contributes to professional development activities such as industrial research and development projects or publication/presentation of papers
- Work with enterprises to develop partnership opportunities on industry projects, curriculum development, and/or training of learners
- Drive cross-functional/project teams focused on developing practical solutions for real-world challenges, with emphasis on translating research into applicable outcomes and providing consultancy for practical implementation
- Perform associated tasks for modules related to Data Engineering, Data architecture, Analytics, Cloud technologies and other ICT-related modules
- Support student development in areas of Education and Career Guidance
- Solicit internship opportunities with industry partners and supervise student internships
- Participate in ad hoc projects pertaining to academic enhancement, student development, publicity and outreach and Continuing Education and Training (CET)
[What we are looking for]
- At least 3 years of relevant work experience in designing, implementing, or supporting data virtualisation solution, big data platforms and pipelines, and/or data lake and data warehouse systems for large‑scale data environments.
- Proficient in Analytical Programming using Python and/or other related programming languages.
- Ability to clearly explain data architecture and data engineering concepts in an applied teaching context.
Additional pre-requisites that are advantageous
- Experience in platforms such as Snowflakes, Denodo, Apache Spark, Hadoop ecosystem, Alteryx or equivalent.
- Experience in designing and building systems for collecting and analysing data (structured and unstructured) at scale with data integration, ETL/ELT processes, data modelling, and performance optimisation.
- Practical exposure to data virtualisation platforms and/or concepts (e.g. virtual data layers, federated queries, abstraction over heterogeneous sources).
- Knowledge of Cloud/Data Engineering DevOps tools such as Azure/AWS Cloud, Apache Spark, Apache Kafka, PostgreSQL, Airbyte and modern data repositories will be an advantage.