MongoDB Architect / Developer
Key Responsibilities
Legacy Data Migration — DART Onboarding (10%)
A critical near-term initiative is migrating WON deals from the DART legacy quoting system into MongoDB Atlas. DART is one of four CPQ heads (alongside SAP CPQ, CDE, and MPC) feeding the D3 platform. This migration is complex for several reasons:
Data Source Challenges:
Oracle DB origin: DART's master data resides in Oracle with a traditional relational schema — normalized tables for quotes, line items, pricing, customers, and approvals joined via foreign keys. This must be transformed into a single deeply nested MongoDB document per deal.
XML export format: DART exports deal data as XML (nightly batch or on-demand trigger for WON quotes). The XML schema does not align 1:1 with the target JSON document model — requiring complex XSLT/programmatic field mapping across 160+ target fields.
Incomplete data: DART XML exports lack several D3-required fields (SAP material numbers, contract terms, fulfillment details, billing schedules).
These gaps are filled by calling real-time REST enrichment APIs during ingestion:
TTSAPOUTPUTPKG API
TTDOSEXTRACT API
Migration Pipeline: DART (Oracle) → XML Export → MuleSoft Validation → S3 Landing Zone → AWS Glue ETL (PySpark) + REST API Enrichment → MDS JSON → S3 Processed Zone → MongoDB Atlas
Your responsibilities:
Design the MongoDB ingestion layer — bulk-write strategies, upsert logic (idempotent re-processing), write-concern tuning for batch loads, and temporary staging collections for validation before promoting to production.
Build aggregation-based data-quality checks — validate that DART-sourced documents meet JSON Schema rules
Implement a dead-letter collection for records that fail validation or API enrichment — with metadata capturing failure reason, retry count, and source XML reference for manual remediation.
Optimize bulk-load performance — tune batch sizes, ordered vs. unordered inserts, index builds (background vs. foreground)