SQL Reconciliation Framework

A SQL-based reconciliation and validation framework that compares source transaction records against downstream financial reporting outputs to detect mismatches, missing records, and variance drivers.

The framework simulates real-world financial reporting validation workflows used by finance and engineering teams to maintain reporting accuracy and audit readiness.

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Resume Project Reference

This repository represents the SQL Reconciliation Framework referenced in my professional experience building financial data validation systems.

The project demonstrates:

• Multi-layer SQL reconciliation logic • Automated validation checkpoints across reporting layers • Break investigation workflows for finance teams • Variance analysis between source and reporting datasets

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Business Problem

In multi-layer financial reporting systems, data flows through several stages:

Source Systems → ETL Pipelines → Reporting Databases → BI Dashboards

At each stage values can diverge due to:

• transformation errors • timing mismatches • missing records • aggregation inconsistencies

Without a systematic reconciliation process, these discrepancies accumulate silently and surface as reporting errors during audits or month-end reviews.

This framework detects discrepancies early and provides structured break analysis.

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What This Framework Does

Step	Description
Classification	Tags each transaction as MATCHED, MISMATCH, or MISSING
Summary Aggregation	Produces counts and variance totals by reconciliation status
Break Investigation	Isolates discrepancy records and assigns severity
Account-Level Rollup	Identifies accounts with recurring discrepancy patterns
Rolling Trend	Tracks discrepancy rate trends over time

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Key SQL Techniques Used

• Common Table Expressions (CTEs) for modular multi-step logic • Window Functions for percentage calculations and rolling trends • Conditional Aggregation using CASE WHEN within SUM() • COALESCE for safe NULL handling • Multi-stage filtering pipelines for discrepancy analysis

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Example Results (Sample Dataset)

Using the included dataset:

• 3 MISSING transactions detected • 3 MISMATCH transactions detected • 30% discrepancy rate across the sample dataset

Accounts flagged for recurring discrepancies:

• Gamma Inc • Zeta Partners

These outputs help analysts quickly isolate break drivers.

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Repository Structure

sql-reconciliation-framework
│
├── reconciliation.sql   ← Full reconciliation logic (5 CTEs + final output)
├── sample_data.csv      ← Sample transaction dataset
└── README.md

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How to Run

1. Load sample_data.csv into a table named transactions
2. Run reconciliation.sql in your SQL environment (PostgreSQL, Snowflake, BigQuery, or DuckDB supported)
3. Review the break report and reconciliation summary outputs

Example quick test using DuckDB:

CREATE TABLE transactions AS
SELECT * FROM read_csv_auto('sample_data.csv');

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Business Impact

Reconciliation frameworks like this help organizations:

• detect reporting discrepancies earlier • reduce investigation time for financial breaks • improve reporting accuracy and audit readiness

In real-world reporting systems, similar validation processes have delivered:

• ~20% reduction in recurring reporting discrepancies • ~30% reduction in break investigation time • Zero critical post-deployment reporting issues across multiple releases

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Author

Yash Sonawane Business Systems Analyst — Financial Data & Reporting

Portfolio · LinkedIn