Automated Data Lineage & Native SQL AST Engine Tools / Tech Stack (Tags): Data Engineering SQL Se...Automated Data Lineage & Native SQL AST Engine Tools / Tech Stack (Tags): Data Engineering SQL Se...
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Rana shahmeer ali
• Aug 6
Automated Data Lineage & Native SQL AST Engine Tools / Tech Stack (Tags):
Data Engineering SQL Server (T-SQL) ETL Automation Data Governance Dynamic SQL C# SQLCLR
The Problem:
In complex regulatory reporting systems, determining why a specific financial account ended up in a specific reporting cell is a massive manual headache. The system stored high-level rules, but the underlying business logic was buried deep inside layers of SQL Views, bit-flags, and General Ledger (GL) mapping tables. When business stakeholders needed answers, developers had to spend hours manually reverse-engineering SQL code and translating raw database IDs into human-readable reasons.
The Solution:
I architected and deployed a 100% automated, zero-dependency Data Lineage Engine natively within SQL Server. This engine acts like an automated developer—it dynamically reads the SQL Views, unpacks the hidden business logic on the fly, cross-references live account data, and translates everything into plain English.
My Contributions & Deliverables:
Native T-SQL AST Parser: Engineered a highly optimized string parsing engine in pure T-SQL. It dynamically reads SQL View definitions, mathematically tracks nested parentheses, and extracts embedded CASE WHEN logic—completely bypassing the need for external Python/CLR dependencies or infrastructure changes.
Multi-Layer Logic Unpacking: Designed an automated ETL pipeline that parses high-level metadata constraints, resolves deep-level bit-flag dependencies, and maps them to dynamic GL account arrays.
Automated Data Translation Engine: Built a dynamic resolution layer that converts raw database IDs and foreign keys (e.g., CountryID = 77) into human-readable business logic (e.g., 'United Kingdom') using a centralized dictionary mapping schema.
Master Orchestration: Delivered a foolproof, idempotent batch processing architecture that loops through thousands of reporting cells and populates a finalized lineage matrix for stakeholders in seconds.
The Impact:
Transformed a manual, hours-long investigative task into a fully automated, one-click pipeline. Business users now have instant access to a transparent, human-readable lineage matrix that guarantees 100% accurate data governance and regulatory compliance reporting—with zero external server dependencies.
Data EngineeringfintechSQLDatabase DevelopmentFinancial AnalysisPython
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