LECTURER, DATA ENGINEERING - SCHOOL OF INFOCOMM TECHNOLOGY

[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; and 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.