Barclays.
“DARS was the kind of internal tool that ages badly. I rebuilt it.”
Barclays runs internal compliance systems where the data is permanent and the database keeps growing. My team's tool — the Data Archival and Retrieval System (DARS) — was supposed to shrink active databases by moving historical rows to cold storage. The previous implementation didn't account for referential integrity, so archival kept stalling on orphaned rows. I rebuilt the dependency resolver and rewrote the archival workflows around it.
The work, with the technical decisions that mattered.
- 50% DB size reduction · 40% query speedup
Rebuilt the DARS backend in Python + Flask. Across 50+ SQL Server tables, the active production DB shrunk by 50% and read-query times improved by 40%. Backup/recovery sped up 30% because the smaller working set fits more easily in standard maintenance windows.
contribution · 01 - Custom topological sort guaranteeing 100% referential integrity
The core algorithmic contribution: a modified Kahn's algorithm that walks foreign-key dependencies recursively and produces a strict archival order. Zero orphaned rows in production runs. The original implementation didn't model dependencies as a DAG, which is why it kept failing on cycles and self-references.
contribution · 02 - Automated workflows + 70% manual ETL overhead reduction
Wrapped archival in batch-validated workflows with structured logging, REST endpoints for compliance teams to trigger ad-hoc archives, and dependency-safe rollback paths. The team's manual ETL touchpoints dropped from ~3 hours/week to ~30 minutes.
contribution · 03
Most legacy-system pain is the previous engineer skipping a single graph-theoretic step (here: it's a DAG, sort it). And big banks reward boring, careful work — the return offer followed because the team trusted the rebuild not to break compliance.
- Built a Python/Flask Data Archival and Retrieval System (DARS), reducing production DB size by 50%, improving query performance by 40%, and backup/recovery speed by 30% across 50+ SQL Server tables.
- Designed a recursive dependency analyzer using a custom topological sort (modified Kahn's algorithm) to guarantee 100% referential integrity during archival.
- Exposed archival workflows via REST APIs with validation, structured logging, and batch execution; automated ETL reduced manual overhead by 70%.
- Ran batch validation across 50+ tables verifying zero orphaned rows post-archival, enabling first-time compliance sign-off on the rebuilt system without rollback events.