Planning Data & Business Intelligence Analyst

About the Role We are seeking a highly analytical Planning Data & Business Intelligence Analyst to support production planning, hydrocarbon accounting, operational performance monitoring, and digital transformation initiatives within a complex industrial environment. The successful candidate will leverage advanced analytics, business intelligence, data engineering, and AI technologies to enhance decision-making, optimize reporting processes, and drive operational excellence. Qualifications & 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 of experience in the Oil & Gas, Energy, Industrial Operations, Manufacturing, or Data Analytics sectors. • Basic understanding of production planning, performance management, and hydrocarbon accounting processes is desirable. • Familiarity with digital technologies, industrial data systems, and business intelligence platforms. • Strong analytical, problem-solving, and critical thinking skills. • Effective communication and presentation abilities. • Interest in and willingness to work with Artificial Intelligence, Machine Learning, and advanced analytics technologies. Key Competencies • Business Intelligence and analytics platforms such as Power BI, Tableau, Qlik, or equivalent tools. • Data engineering, SQL development, ETL processes, and data integration. • AI/ML technologies including Python, TensorFlow, PyTorch, and related frameworks. • Data visualization, dashboard development, and executive reporting. • Advanced Microsoft Excel skills, including data modelling, trend analysis, and reporting automation. • Familiarity with production data management systems and operational performance monitoring. • Proficiency with presentation and process visualization tools such as PowerPoint and Visio. • Experience in digital transformation, reporting automation, and data-driven decision support. Key Responsibilities • Design, develop, and maintain Business Intelligence dashboards supporting production planning, operational performance, and business reporting. • Streamline and automate reporting workflows using Power BI, Tableau, Qlik, or equivalent analytics platforms. • Create interactive visualizations and management reports to support decision-making at operational and leadership levels. • Integrate and consolidate data from multiple operational and business systems. • Ensure data quality, consistency, reliability, and governance across various data sources. • Develop, maintain, and optimize ETL processes, data pipelines, and analytical datasets. • Build and support AI/ML models for forecasting, anomaly detection, performance optimization, and decision support. • Contribute to predictive analytics initiatives focused on reliability, operational efficiency, and loss prevention. • Support integration of advanced analytics solutions with production, planning, and business systems. • Perform scenario modelling and "what-if" analysis to support operational and business optimization. • Automate manual reporting and planning activities using AI, RPA, and advanced digital technologies. • Maintain robust data governance, security, compliance, and auditability standards for analytical outputs. • Deliver training, user support, and knowledge-sharing sessions to business stakeholders. • Collaborate with Operations, Engineering, Digital, IT, and Business teams to identify and implement data-driven improvement opportunities. Additional Requirements • Candidates must demonstrate strong analytical capabilities, data management expertise, and the ability to work effectively across multidisciplinary teams. • Experience working with operational, industrial, or energy-sector data environments is highly desirable. • Ability to support both corporate and operational site activities when required. • Successful candidates will undergo technical and competency-based assessments as part of the selection process. Show more Show less

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 of experience in the Oil & Gas, Energy, Industrial Operations, Manufacturing, or Data Analytics sectors. Familiarity with digital technologies, industrial data systems, and business intelligence platforms. Strong analytical, problem-solving, and critical thinking skills. Effective communication and presentation abilities. Interest in and willingness to work with Artificial Intelligence, Machine Learning, and advanced analytics technologies.

Design, develop, and maintain Business Intelligence dashboards supporting production planning, operational performance, and business reporting. Streamline and automate reporting workflows using Power BI, Tableau, Qlik, or equivalent analytics platforms. Create interactive visualizations and management reports to support decision-making at operational and leadership levels. Integrate and consolidate data from multiple operational and business systems. Ensure data quality, consistency, reliability, and governance across various data sources. Develop, maintain, and optimize ETL processes, data pipelines, and analytical datasets. Build and support AI/ML models for forecasting, anomaly detection, performance optimization, and decision support. Contribute to predictive analytics initiatives focused on reliability, operational efficiency, and loss prevention. Support integration of advanced analytics solutions with production, planning, and business systems. Perform scenario modelling and "what-if" analysis to support operational and business optimization. Automate manual reporting and planning activities using AI, RPA, and advanced digital technologies. Maintain robust data governance, security, compliance, and auditability standards for analytical outputs. Deliver training, user support, and knowledge-sharing sessions to business stakeholders. Collaborate with Operations, Engineering, Digital, IT, and Business teams to identify and implement data-driven improvement opportunities.