AWS Certified Data Engineer with 1.5+ years of production experience building scalable pipelines, streaming architectures, and ML-ready data systems. I bridge the gap between raw data and real decisions — whether that's a Medallion Architecture warehouse in Snowflake, a RAG pipeline powered by LangChain, or a low-latency FastAPI service at scale.
- 🎓 MSCS @ Stevens Institute of Technology, Hoboken NJ — GPA: 3.709 / 4.0 (Exp. May 2026)
- ☁️ AWS Certified Data Engineer – Associate (Mar 2026)
- 📍 Based in Union City, NJ | Open to full-time roles in Data / AI / Software Engineering
- ⚡ Reduced pipeline latency by 35%, cut compute costs by 40%, processed 500k+ records/day
- 🌐 View my interactive portfolio →
| Metric | Result |
|---|---|
| Daily records processed | 500,000+ across 40+ clients |
| Pipeline latency reduction | 35% via parallelism & scheduling |
| Compute cost savings | 40% via Incremental Materialization |
| Manual effort eliminated | 15+ hrs/month through Airflow automation |
| Legacy data migrated | 200GB+ to modern cloud frameworks |
| SQL performance gain | 25% through indexing & columnar storage |
[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░░] Streaming pipeline with Kafka + Spark + dbt
[▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░] RAG system with local LLMs and ChromaDB
[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░] AWS Certified Solutions Architect prep
[▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░░░] MSCS coursework @ Stevens (GPA 3.709)
AWS S3 Kafka Spark Snowflake dbt Core Airflow Docker GitHub Actions
- Medallion Architecture (Bronze/Silver/Gold) with Kafka + AWS Kinesis for real-time ingestion
- Metadata-driven Gold layer with dbt Jinja macros; SCD Type 2 snapshots for point-in-time accuracy
- Prometheus monitoring + dbt freshness checks; 40% compute cost reduction via Incremental Materialization
- Containerized with Docker; automated deployments via GitHub Actions CI/CD
Python FastAPI LangChain ChromaDB HuggingFace SQLite
- Zero-cost RAG backend using local HuggingFace embeddings (384-dim) — no cloud API dependency
- Persistent ChromaDB + SQLite vector store; sub-second retrieval over 400+ page documents
- Full OOP-driven modular design with REST API endpoints via FastAPI for production inference
Data Engineer · Rajlaxmi Solutions Private Limited · May 2023 – June 2024
- Production ELT pipelines processing 500k+ daily records in Python, Spark & Airflow
- Snowflake warehouse design with Medallion Architecture & dimensional modeling
- Schema validation, anomaly detection & SLA alerting — contributed to an Industry Excellence Award
Data Analyst Intern · Rajlaxmi Solutions Private Limited · Jan – May 2023
- Migrated 200GB+ legacy databases to cloud; 25% SQL performance improvement
- Automated Airflow reporting workflows — eliminated 15+ hrs/month of manual work
- 🏆 AWS Certified Data Engineer – Associate — Amazon Web Services, April 2026
- 🏆 AWS Academy Data Engineering — Amazon Web Services, April 2026
- 📧 adhandha@stevens.edu
- 📍 Union City, NJ (open to remote & hybrid)
⚡ Fun fact: I once eliminated 15 hours of manual work a month with a single Airflow DAG — and haven't looked back.
