Version-controlled resume aligned to hands-on data engineering, platform architecture, and AI-driven systems.
I design and build data platforms that treat pipelines as products, emphasizing:
- Data quality and governance
- Observability and operational metrics
- Scalable ingestion and transformation patterns
- Hybrid deployment constraints (on-prem + cloud)
- Kafka-based streaming architectures
- Medallion (Bronze/Silver/Gold) pipelines
- PySpark / distributed processing
- Data quality enforcement and quarantine design
- Hybrid data systems (on-prem + cloud UI/API)
- API-first data access patterns
- Decoupled ingestion, processing, and serving layers
- Data lifecycle ownership
- Retrieval-Augmented Generation (RAG) pipelines
- Vector databases (Qdrant)
- Embedding pipelines and semantic search
- LLM integration into data platforms
- Kubernetes (k3s)
- FastAPI services
- systemd-based orchestration
- Local-first development environments
The following repositories demonstrate end-to-end data platform capabilities across ingestion, processing, observability, and AI-driven retrieval.
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Dev Stream
Streaming ingestion system with partitioned landing zone and replayable data architecture -
Hybrid Metrics Platform
Hybrid data platform exposing pipeline observability metrics via API -
RAG with Kubernetes Documentation
Production-style RAG pipeline with Bronze/Silver/Gold processing and vector search -
Context-Aware Pareto Optimization
Experimental framework for modeling trade-offs and decision-making using context-aware Pareto optimization
This resume is maintained as a living document:
- Updated alongside project work
- Aligned with current technical focus
- Structured for both ATS and technical review
This repository reflects:
- A systems-oriented approach to data engineering
- Platform-level architectural thinking
- A focus on production-aligned solutions