Data & Digital Integration Engineer

About the Role

We are seeking a Data & Digital Integration Engineer to develop and deliver data-driven solutions, automation and advanced analytics that improve operational performance across a major energy production environment.

Working closely with multidisciplinary engineering, production and technology teams, you will identify opportunities to leverage data, artificial intelligence and machine learning to improve decision-making, optimise processes and deliver measurable business value.

This is an opportunity to combine technical data expertise with practical engineering and operational knowledge, translating complex datasets into scalable digital solutions that make a real difference to business performance.

Key Responsibilities

• Identify and develop digitalisation and data integration opportunities to improve operational efficiency and business performance.
• Apply AI, machine learning and advanced analytics to identify inefficiencies, trends, anomalies and opportunities for improvement.
• Translate business requirements across production optimisation, forecasting and hydrocarbon accounting into practical digital solutions and workflows.
• Develop and maintain data pipelines, analytical models and digital products, ensuring reliability, performance and ongoing improvement.
• Monitor deployed models and data solutions, identifying performance issues, managing defects and supporting their ongoing lifecycle.
• Integrate data insights into operational platforms and systems, including PI, SCADA, GIS, SAP and asset management environments.
• Collaborate with engineering, production, maintenance and technology stakeholders to deliver fit-for-purpose solutions.
• Maintain technical documentation, data definitions and operating procedures in accordance with relevant governance, cybersecurity and architectural standards.
• Track project delivery, assess business outcomes and monitor the value generated through digital initiatives.

Skills & Experience: To be successful in this role, you will ideally bring:

• A degree in Engineering, Data Science, Computer Science or a related discipline.
• Strong technical experience with Python, SQL, machine learning libraries, statistical modelling and large-scale time-series analytics.
• Experience in data wrangling, feature engineering, pattern recognition, anomaly detection and time-series analysis.
• Familiarity with machine learning techniques and analytical methods such as Dynamic Time Warping (DTW), correlation analysis and Matrix Profile.
• Exposure to cloud data platforms and technologies such as Spark, Databricks, Azure Machine Learning, MLOps, CI/CD and Git.
• Experience working with production, maintenance, planning or engineering data environments.
• Familiarity with operational systems such as PI, SCADA, SAP, Maximo or equivalent platforms.
• The ability to translate complex technical and operational data into meaningful insights and recommendations.
• Strong communication and stakeholder engagement skills, with the ability to work across technical and non-technical teams.
• Demonstrated ability to manage competing priorities, solve complex problems and deliver initiatives in a changing business environment.

Experience within the energy, oil and gas or LNG sectors would be advantageous, particularly exposure to coal seam gas operations and hydrocarbon accounting or allocation processes.

However, relevant experience from other industrial or engineering environments will also be considered.