Initializing System...
Initializing System...
Making sense of financial data from 20+ ERP systems.
A complex finance transformation environment shaped into a traceable Databricks pipeline, with schema normalisation, validation logic, reconciliation checks, and audit-ready financial modelling.
Data Transformation
Large-scale financial data from multiple ERP systems required structured cleaning, transformation, and analysis under strict compliance requirements. Data discrepancies between source systems often delayed delivery and necessitated extensive manual reconciliation.
A professional services environment supporting financial audit teams across multiple sectors and geographies. The nature of the work required every transformation to be documented, reproducible, and auditable — both for regulatory compliance and client trust.
An automated Databricks pipeline designed to ingest, transform, validate, and reconcile raw finance data from multiple ERP schemas. The architecture enforces quality gates and produces an auditable financial model for enterprise analysis.
Azure Databricks as the central processing layer, ingesting raw ERP exports, applying validated transformation logic, and producing structured outputs for analyst consumption.
Accuracy and traceability were non-negotiable. The downstream cost of a data error in an audit context is extremely high — not just rework, but reputational risk. Every design decision was made through the lens of: "Can an independent reviewer understand exactly what this transformation does and why?" Focused on reducing data discrepancies while accelerating delivery to audit teams.
The primary engineering challenge was building a normalisation layer that could handle the structural differences between 20+ ERP schemas without becoming a maintenance nightmare. Solved this with modular PySpark functions, one per ERP type, sharing a common output contract. Automated reconciliation checks against source system control totals caught discrepancies before they propagated.