Data Analyst II - Finance and Commercial

Amsterdam · 2-4 years experience · Remote · Posted Oct 2, 2026

At a glance

In this role, you will transform complex data into clear insights that guide commercial decision-making.
You will contribute to the development of scalable analytics solutions, from dashboards to data pipelines, while collaborating closely with senior data analysts, data engineers, and cross-functional partners across the business.

Assist in the development and optimization of Revenue Operations data pipelines using SQL, Python, and big-data tools.
Contribute to ETL improvements to ensure data consistency and reliability.
Build and maintain LookML models and Looker dashboards to deliver accurate, timely insights to commercial stakeholders.
Play an active role in data governance initiatives by cleaning, transforming, and documenting datasets, ensuring accuracy and reliability.
Contribute to the automation of recurring reporting processes (e.g., Quarterly Business Reviews, pipeline reporting).
Develop scalable, reliable, and sustainable data products that can be re-purposed for different use cases.
Work closely with cross-functional data teams and other Revenue Operations functions to align on data initiatives and technical improvements.
Analyze datasets and translate findings into actionable insights for the commercial teams.

2-4 years of experience in Data Analytics with proven expertise in the field.
Proficiency in Python and SQL for data analysis and processing.
Good communication and collaboration skills, with the ability to work effectively within a global, multicultural team.
Data Wrangling expertise: skilled in cleaning, transforming, and organizing large datasets, handling missing data, detecting outliers, and ensuring data integrity.
Good analytical skills: adept at extracting meaningful insights from complex datasets and translating them into actionable recommendations.
Flexibility for occasional business travel to support global collaboration and projects.

Familiarity with analytical and statistical techniques (e.g., distributions, correlations, hypothesis testing, regression analysis) and with validation frameworks.
Exposure to big-data tools such as PySpark, Airflow or Git.
A background in econometrics, data science, engineering, or other quantitative studies.

This role is based out of the Amsterdam office.
We are an office-first company and value in-person collaboration; we do not offer remote-only roles.