Analytics Engineer Mid

Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses ‘go beyond’.

The Analytics Engineers who serve these teams own the data modelling layer that all of it runs on: dbt models, metric definitions, and semantic layer contributions. must have

Maintain data quality in your domain through dbt tests, freshness SLAs, and proactive monitoring. must have

• SQL Solid SQL and dbt experience, with a clear comfort owning models end to end. must have
• dbt Build and maintain dbt models in our new Analytics Warehouse, using a clean, layered architecture and migrating logic off legacy tools as you go. must have
• You'll join a close-knit team actively migrating our analytics stack onto a new Analytics Warehouse and Omni, moving off legacy tools like Looker and Hippocampus. not practised here
• Warehousing You'll join a close-knit team actively migrating our analytics stack onto a new Analytics Warehouse and Omni, moving off legacy tools like Looker and Hippocampus.
• Airflow / orchestration
  - Orchestration:
  Airflow
• - Maintain data quality in your domain through dbt tests, freshness SLAs, and proactive monitoring.
• GCP
  - Data warehouse: GCP / Big Query (new Analytics Warehouse) not practised here