Role Purpose:
- We are seeking a highly skilled Data Engineer or Data Scientist whose core strength
is Enterprise Data Modelling.
- The successful candidate will design scalable enterprise data models that underpin
modern data platforms, analytics, artificial intelligence and machine learning
solutions.
- This role combines enterprise data modelling with modern data
engineering and practical machine learning to deliver robust, production-ready
solutions for enterprise clients.
- Working closely with clients and delivery teams, you will translate complex business
domains into scalable data architectures while developing cloud-native data
platforms that enable advanced analytics.
- This is a hands-on engineering requiring excellent technical capability, strong
problem-solving skills and experience delivering enterprise data solutions from
design through deployment
Key Responsibilities
Data Modelling & Architecture
• Design conceptual, logical and physical enterprise data models.
• Model complex business domains and large-scale datasets.
• Design schemas for transactional, analytical and AI workloads.
• Develop enterprise information models that enable reporting analytics and
machine-learning.
• Optimise enterprise data models for scalability, maintainability and performance
Data Engineering
• Design and develop scalable ETL/ELT pipelines.
• Build reliable data ingestion pipelines for structured, semi-structured and
streaming data.
• Develop modern, cloud-based data platforms.
• Optimise data transformation, orchestration and storage.
• Support batch and real-time processing requirements.
• Ensure high levels of data quality and operational reliability.
Data Science & Machine Learning
• Develop statistical and machine learning models to solve business problems.
• Apply regression, classification, clustering, forecasting, anomaly detection and
optimisation techniques.
• Engineer features for production machine learning workloads.
• Evaluate, monitor and improve model performance.
Production Deployment
• Deploy machine learning solutions into production environments.
• Support model lifecycle management and monitoring.
• Work within established CI/CD deployment pipelines.
• Apply software engineering best practices to data and AI solutions.
• Build production APIs and inference services supporting AI and analytics
workloads.
Cloud-native Data Platforms
• Build and deploy data and AI solutions on Google Cloud Platform (preferred) or
AWS.
• Develop cloud-native data solutions using managed cloud services.
• Use Docker and modern software engineering practices to support deployments.
Technical Competencies
Programming
• Python (Expert)
• SQL (Expert)
• Bash/Shell
• Java or C/C++ advantageous
Enterprise Data Modelling
• Conceptual Data Modelling
• Logical Data Modelling
• Physical Data Modelling
• Enterprise Information Architecture
Data Governance
• Data Lineage
• Metadata Management
• Data Quality
• Data Warehousing
• Dimensional Modelling
Data Engineering
• ETL/ELT
• Apache Kafka
• Airflow
• Pub/Sub
• BigQuery
• PostgreSQL
• Iceberg
• Parquet
• Streaming Data
• Data Warehousing
Machine Learning & Data Science
• Scikit-learn
• PyTorch
• XGBoost
• MLflow
• Vertex AI
• TensorFlow (advantageous)
• Statistical Modelling
• Time-series Forecasting
• Optimisation
• Feature Engineering
Cloud Technologies
• Google Cloud Platform (preferred)
• AWS
• Docker
• Git
• CI/CD Pipelines
Generative AI (Highly Desirable)
• Retrieval-Augmented Generation (RAG) Architectures
• LangChain / LangGraph
• Agentic Workflows
• Weaviate
• Model Context Protocol (MCP)
• LLM Evaluation
Essential Requirements
Intermediate
• Bachelor’s degree or advanced degree in Computer Science, Data Science,
Statistics, Mathematics, Engineering or a related quantitative discipline.
• 4–6 years’ experience in Data Engineering, Data Science or Machine
Learning Engineering.
• Demonstrated experience designing enterprise data models.
• Strong Python and SQL development skills.
• Experience building cloud-native data platforms.
• Experience deploying data or machine learning solutions into production.
• Experience working with Google Cloud Platform (preferred) or AWS.
Senior
• Bachelor’s degree or advanced degree in Computer Science, Data Science,
Statistics, Mathematics, Engineering or a related quantitative discipline.
• Extensive experience designing enterprise data models for complex business
domains.
• Expert Python and SQL.
• Demonstrated ability to design enterprise data models for complex business
domains.
• Proven experience leading technical solution design.
• Experience mentoring engineers and contributing to architecture decisions.
• Experience working with Google Cloud Platform (preferred) or AWS