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The challenge
Moving legacy reporting into a centrally governed Data Lake required more than rebuilding SQL in a new platform. Years of business logic, ETL behavior, dependencies, and reporting expectations had to be understood, documented, translated, and validated so critical analytical capabilities were not lost during migration.
My role
I served as a bridge between legacy SQL implementations, business processes, and the future-state architecture, working with consultants and internal teams to explain how existing solutions functioned and what needed to be preserved.
Documentation & Agile delivery
- Documented source data, business rules, ETL transformations, dependencies, data flows, reporting requirements, and technical processes in Confluence.
- Worked within an Agile delivery process using Jira tickets and Jira boards to track development, validation, issues, and requirement changes.
- Collaborated with developers and consultants as questions and discrepancies moved through the development lifecycle.
Migration & validation
- Performed source-to-target and data-quality validation across DEV, TEST, and PROD.
- Checked data types, transformations, business rules, mappings, and expected business outcomes.
- Identified discrepancies and worked with project teams to resolve them before production use.
- Completed downstream validation by confirming migrated data assets connected correctly to Power BI and produced expected reporting results.
How this connected to earlier lineage work
The migration benefited from prior lineage and documentation work that exposed unused views, redundant objects, technical debt, and ETL optimization opportunities. That created a clearer picture of what should be preserved, cleaned up, or redesigned rather than simply copied forward.
Impact
Helped preserve critical business and analytical knowledge while transitioning legacy reporting into a governed enterprise Data Lake, improving documentation, traceability, data quality, and confidence in the migrated BI solutions.