Qubika

The client operated fragmented financial data systems that resulted in a five-day bank close cycle, approximately 10,000 analyst hours of manual reconciliation effort each month, and significant compliance risks arising from more than 1,500 financial controls. The organization required a modern financial data platform to automate reconciliation, improve auditability, and provide timely access to trusted financial data.

Qubika modernized the client's financial data backbone by implementing a cloud-based financial data platform using Amazon Redshift, Amazon EMR, Apache Spark, Apache Airflow, and Apache Iceberg. The solution automated volatility classification, financial reconciliation, and audit workflows through rules-based logic while incorporating LLM-assisted SQL agents to improve operational efficiency and support enterprise financial reporting.

The client's mission-critical lakehouse platform was approaching end-of-life due to a legacy Databricks runtime supporting production data pipelines that were tightly integrated with machine learning workloads. The organization required a controlled platform upgrade without disrupting production operations while ensuring long-term platform stability and governance.

Qubika executed a phased upgrade of the Databricks lakehouse platform across development, user acceptance testing, and production environments. The implementation standardized platform dependencies, resolved compatibility issues, and introduced rollback safeguards at every stage to ensure uninterrupted production operations while establishing a resilient and governance-ready data platform.