AI-Driven Finance Data & Analytics Engineer

Freelio; København; Hybrid; DKK 700,000 - 900,000; Full time

Job summary

Ramboll in Denmark is seeking a Data & Analytics Engineer to join our Global Data & Analytics team at our head office. You will design data solutions, structure data, and create visualizations that support Finance reporting and decision making, using Microsoft Fabric.

You will collaborate across functions to translate business needs into scalable technical solutions and explore how AI can enhance analytics workflows while strengthening data quality and usability.

Qualifications

• A relevant degree in data, IT, business analytics, finance, engineering or AI.
• Solid knowledge of data analytics and visualization.
• Interest in modern data platforms such as Microsoft Fabric.
• Curiosity about AI to strengthen analytics solutions.
• Structured, analytical problem-solving with good collaboration.

Responsibilities

• Build and support data solutions in Microsoft Fabric for Finance use cases.
• Handle data extraction, transformation, modelling and structuring.
• Create dashboards, reports and visualizations.
• Collaborate with Finance to translate business needs into technical solutions.
• Ensure high data quality, consistency, and usability.
• Advance our data and analytics capabilities.
• Explore AI integration to add value to workflows.

Skills

Data analytics

Data visualization

Microsoft Fabric

AI curiosity

Structured problem-solving

Communication skills

Education

Relevant degree in data/IT/analytics/engineering/AI

Tools

Power BI

SQL

Microsoft data tools

Job description

Ramboll in Denmark is seeking a Data & Analytics Engineer to join our Global Data & Analytics team at our head office. You will design data solutions, structure data, and create visualizations that support Finance reporting and decision making, using Microsoft Fabric.

You will collaborate across functions to translate business needs into scalable technical solutions and explore how AI can enhance analytics workflows while strengthening data quality and usability.