Planning Data & Business Intelligence Analyst

About the role

Qualifications and experience

• Bachelor’s degree in Engineering, Data Science, Computer Science, Information Systems, or a related discipline.
• Minimum 10 years of relevant professional experience.
• At least 5 years in Oil & Gas, Energy, Industrial Operations, Manufacturing, or Data Analytics sectors.
• Basic understanding of production planning, performance management, and hydrocarbon accounting is desirable.
• Familiarity with digital technologies, industrial data systems, and BI platforms.
• Strong analytical, problem-solving, and critical-thinking skills; effective communication and presentation abilities.
• Interest in and willingness to work with AI, machine learning, and advanced analytics.
• Experience with operational, industrial, or energy-sector data environments is highly desirable.
• Able to support corporate and operational site activities when required.

Technical competencies

• BI and analytics platforms such as Power BI, Tableau, Qlik, or equivalent.
• Data engineering, SQL development, ETL, and data integration.
• AI/ML technologies, including Python, TensorFlow, PyTorch, and related frameworks.
• Data visualization, dashboard development, and executive reporting.
• Advanced Excel for data modeling, trend analysis, and reporting automation.
• Production data management systems and operational performance monitoring.
• PowerPoint and Visio for presentations and process visualization.
• Digital transformation, reporting automation, and data-driven decision support.

Key responsibilities

• Design, develop, and maintain BI dashboards supporting production planning, operational performance, and business reporting.
• Automate reporting workflows and create interactive visualizations and management reports.
• Integrate and consolidate data from operational and business systems; maintain data quality, reliability, and governance.
• Develop and optimize ETL processes, data pipelines, and analytical datasets.
• Build AI/ML models for forecasting, anomaly detection, performance optimization, and decision support.
• Contribute to predictive analytics initiatives for reliability, operational efficiency, and loss prevention.
• Integrate advanced analytics with production, planning, and business systems; perform scenario and what-if analysis.
• Automate manual reporting and planning using AI, RPA, and other digital technologies.
• Maintain security, compliance, and auditability standards for analytical outputs.
• Deliver training, user support, and knowledge-sharing sessions to stakeholders.
• Collaborate with Operations, Engineering, Digital, IT, and Business teams on data-driven improvements.

Selection process

• Successful candidates will complete technical and competency-based assessments.