Data Analyst

Are you motivated by turning complex data into clear insight and practical business value? The Global Data Intelligence team in DNV Energy Systems is looking for a Data Analyst to join our team in Oslo and help shape how data is used in our regions and functions to support better decisions, stronger performance, and more effective ways of working. We deliver the insights portal for Energy Systems, we participate in improvements projects with Operational Excellence teams, and we engage in AI adoption initiatives. In this role, you will work with large and complex datasets from multiple sources, transforming data into trusted analytics, dashboards, and visual stories that make insights accessible to stakeholders across the business. You will combine curiosity, analytical rigour, and strong business understanding to identify patterns, explain performance, and support data-driven decision-making. You will be part of a collaborative analytics environment where data quality, usability, and stakeholder adoption matter as much as technical delivery. The position is well suited for someone with 2–4 years of relevant experience who enjoys working hands-on with business intelligence tools and translating analytical findings into clear recommendations. What you will do Analyse large, complex datasets to identify trends, patterns, risks, and opportunities that support business priorities. Design, build, and maintain effective dashboards and reports using Power BI. (Experience with Tableau, Qlik, or similar business intelligence tools is valuable) Translate business questions into analytical approaches, define relevant measures and KPIs, and present findings in a clear and actionable way. Work closely with business stakeholders, data engineers, product teams, and subject matter experts to understand requirements and improve analytical solutions. Contribute to improving reporting processes, data models, data definitions, and self-service analytics capabilities, tied closely with DNV wide initiatives Engage in the adoption of AI across Energy Systems, including identifying, exploring, and supporting relevant agent and agentic use cases that can improve analytics, decision support, and ways of working.