BillGO is building the next generation of payment and money movement infrastructure for small businesses. Data products and insights are core to how we scale reliability, reduce operational friction, and deliver better outcomes across payments, risk, and support. 

We are looking for an Analytics Engineer to join our growing data team and help transform payments data into reliable, scalable, and actionable insights. Sitting at the intersection of data engineering, analytics, and business, you will build clean data models, define key metrics, and enable stakeholders across product, finance, risk, and operations to make data-driven decisions. 

 This role is ideal for someone who understands the complexity of payments ecosystems (transactions, settlements, fraud, reconciliation) and enjoys turning messy data into trusted, well-documented datasets. 

💥 Why This Role Matters

This role matters because it transforms complex, fragmented payments data into a trusted foundation for decision-making across the business. As an Analytics Engineer, you enable teams to operate with clarity and confidence by building reliable data models, defining consistent metrics, and ensuring data integrity in a highly intricate financial ecosystem. In a space where accuracy, speed, and compliance are critical, your work directly impacts everything from revenue visibility and reconciliation to fraud detection and operational efficiency. By bridging the gap between raw data and business insight, this role not only improves day-to-day performance but also helps scale the company’s data infrastructure, empowering smarter decisions and driving long-term growth in a rapidly evolving fintech landscape.

🚀 What You’ll Do

• Design, build, and maintain scalable data models for customers, payments, transactions, settlements, and financial reporting  


• Transform raw data into clean, reliable datasets and data models. 
  
  
  
  • +Using tools like Claude, Coalesce, and Tableau  
  
  
  • Using data warehouses like Snowflake, AWS RDS, AWS DynamoDB 
  
  
  • Also incorporating other data sources from AWS S3, like csv, Json, and parquet files. 
  
  
  
  


• Develop data dictionaries, semantic layers, and data catalogs 


• Partner with Product, Finance, Risk, and Operations teams to define key metrics and enable insights and dashboards for decision-making and predictions.  


• Ensure data quality and integrity through testing, monitoring, and documentation  


• Optimize data pipelines for performance and cost efficiency  


• Translate business requirements into data solutions that scale with company growth  


• Support regulatory and financial reporting needs (e.g., reconciliation, audit readiness)  


• Contribute to data governance, definitions, and metric standardization