Projects

Context

Department-wide reports depended on consistent data delivery and traceable transformations.

Challenge

High reliability expectations with enterprise infrastructure constraints and a strict production change process.

Technical approach

Designed metadata-driven ETL processes with clear data models, logging tables, and operational runbooks. Coordinated requirements with report designers and validated behavior under production-like volumes.

Outcome

Established a stable and traceable ETL foundation for department-wide reporting, significantly improving incident diagnosis and reducing operational support effort during reporting cycles.

Context

Enterprise data models required reliable initial loads at billion-scale and stable daily delta processing under strict operational constraints.

Challenge

Standard ETL approaches failed due to runtime limits, lack of observability, and insufficient error handling for production orchestration.

Technical approach

Built a metadata-driven batch loader with partition-aware processing and scheduler-integrated error handling to meet enterprise runtime and observability requirements.

Outcome

Enabled predictable runtimes for billion-scale initial loads and reliable daily delivery of million-scale deltas, significantly reducing operational incidents and manual recovery effort.

Context

Teams needed structured, machine-readable access to enterprise Confluence documentation for downstream RAG and embedding workflows.

Challenge

Confluence pages contained inconsistent HTML artifacts and required reliable source attribution per chunk to support knowledge base retrieval — ad-hoc extraction provided neither.

Technical approach

Built a Scrapy-based ETL pipeline with six ordered stages: REST API extraction, metadata validation, HTML-to-Markdown conversion, dual-format sidecar export, local staging and optional S3 upload. All metadata fields are derived deterministically.

Outcome

Production-ready document dataset of ~800 Confluence pages per monthly run, directly consumable for AWS Bedrock Knowledge Base embedding with per-page content quality signals and portable metadata for alternative vector stores.