Data Engineering

• Design and manage high-throughput, low-latency data pipelines using distributed computing frameworks.
• Build scalable ETL/ELT workflows using tools such as Airflow and Spark.
• Work with containerised environments (e.g., Kubernetes, OpenShift) and real‑time data platforms (e.g., Apache Kafka, Flink).
• Ensure efficient data ingestion, transformation, and integration from multiple sources.
• Maintain data integrity, reliability, and governance across systems.

Data Analysis & Modelling

• Apply statistical and machine learning techniques to analyse data and translate complex data sets to identify patterns, trends and actionable insights that drive business strategy and operational efficiency.
• Develop predictive models, recommendation systems, and optimisation algorithms to solve business challenges and enhance operational efficiency.
• Transform raw data into meaningful ...