Develop and maintain dashboards, KPIs, and analytical deliverables with end-to-end accountability for data accuracy, metric definitions, and stakeholder usability.
Use SQL and Power BI to query, model, and visualize data — building reliable reports that support business decision-making.
Collaborate with commercial, product, and Data Engineering teams to translate requirements into clear, actionable analytics.
Monitor data quality, flag inconsistencies early, and support corrective actions with engineering partners.
Contribute to metric standardization and documentation — helping ensure consistent definitions across reports and tools.
Work in Git-based workflows (branches, pull requests) for SQL and BI assets, following team standards for versioned analytics delivery.
Support team initiatives such as centralized KPI layers, structured self-service, and the migration toward more robust analytics operations — including CI/CD for BI where applicable.
Deliver ad hoc analyses with limited supervision, connecting insights to business questions and recommending next steps.

BA/BS in Math, Statistics, Economics, Engineering, Computer Science, or related quantitative field.
Advanced SQL for querying, transformation, and analytical modeling.
Advanced Power BI — data modeling, DAX, and dashboard design.
Basic Python and PySpark — able to read and interpret scripts; complex pipeline work done with Data Engineering support.
Intermediate Statistics — sound metric interpretation and support for data-driven decisions.
Experience with Git and collaborative development workflows.
2–4 years in data analytics with a track record of delivering KPIs and dashboards in cross-functional environments.
Advanced English (spoken and written) for global collaboration.

Mid Level Data Analyst

Key skills