Data Analyst II - Finance and Commercial

Data Analyst

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.

What you’ll do

• Support technical contributions: 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.
• Reporting and dashboards: Build and maintain LookML models and Looker dashboards to deliver accurate, timely insights to commercial stakeholders
• Data quality and governance: Play an active role in data governance initiatives by cleaning, transforming, and documenting datasets, ensuring accuracy and reliability.
• Automation: Contribute to the automation of recurring reporting processes (e.g., Quarterly Business Reviews, pipeline reporting)

Scalability: Develop scalable, reliable, and sustainable data products that can be re-purposed for different use cases.Collaboration: Work closely with cross-functional data teams and other Revenue Operations functions to align on data initiatives and technical improvements.

• Developing insights: Analyze datasets and translate findings into actionable insights for the commercial teams.

Who you are

Must haves:

• 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

Plus:

• 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