Analytics Engineer, Mollie

• 2+ years experience as data analyst, analytics engineer or data scientist using data to drive success
• Strong problem solving skills and attention to detail
• Proactive, pragmatic and have a ‘can do’ attitude
• Strong stakeholder management and communication skills
• Fluency in SQL (we use BigQuery)
• Experience working with a BI tool (we use Looker)
• Experience with data modelling (we use dbt)
• Experience working with Git

Desirable

• As an Analytics Engineer in our Revenue Operations domain, you will collaborate with our commercial teams, including sales, customer success and partnerships
• You will support their decision making by providing actionable insights that they need to drive commercial value and enhance efficiency
• You will also help them to extract insights themselves through our self-serve reporting tools
• You will work closely alongside the Revenue Operations cross-functional team, other commercial teams and software engineers to ensure we collect, store and report the correct and relevant data
• Then working alongside other data professionals, you’ll build robust data models, dashboards, perform in-depth analyses and tell compelling stories to steer our commercial teams in the right direction
• Data modelling: transform data from different sources into clean, robust, performant, easy to use data models based on the analytical needs of your stakeholders
• Commercial steering: be the owner of key steering dashboards allowing commercial teams to track their performance against targets as well as drive action to improve the performance
• Data storytelling: turn your insights into compelling stories and share them via presentations, blog posts, memos, dashboards, workshops, or whatever works best!
• Business Intelligence: build and maintain robust, performant data models (dbt + Looker) to empower your stakeholders to report their most important KPIs
• Product development: work closely with the domain leadership team and engineers to make sure our products synthesise the data required to measure commercial performance
• Craft development: contribute to the evolution of the data analytics craft by sharing your insights, challenges and being a vocal member of the community