- 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), and temporary index suppression during large migrations to maximize throughput.
- Track lineage — maintain full audit trail from DART Oracle source → S3 raw XML → enriched JSON → MongoDB document,
enabling traceability for compliance and data-quality investigations.
Schema Design, Data Modelling and implementation (40%) MongoDB Atlas Administration & Operations (5%)
Indexing & Query Optimization (5%)
Integration & Pipeline Development (35%)
Performance Engineering & Reliability (5%)
Required Qualifications
MongoDB Certification (Mandatory)
Hold at least one active MongoDB certification:
Education & Experience
- Bachelor's degree in Computer Science, Data Engineering, or a related field — or equivalent practical experience.
- 5+ years of professional experience with MongoDB in production environments, with at least 2 years on MongoDB Atlas (cloud-managed).
- 3+ years designing schemas for complex, deeply nested document structures (not straightforward flat CRUD collections).
- Experience supporting enterprise-grade workloads — multi-tenant, multi-region, with strict latency and availability SLAs.