Why Interview Me
I build automation, data engineering, and integration solutions that solve real business problems. If you need a Python | Automation | Data Engineering | System Integration engineer who can design, deploy, and document reliable production systems, I am ready.
DP-700, Databricks DE Associate, Google Data Analytics, and more.
Contributions to Dagster, urllib3, axios, fastify, and more.
Comprehensive test coverage by unittest/pytest scripts across projects.
Reduced deployment time through configuration-driven automation.
What I bring in 20 seconds
- Platform mindset: I design reusable, config-driven automation and data platforms, not one-off scripts.
- Modern stack: Python, FastAPI, SQL, PySpark, Kafka, Delta Lake, Docker, CI/CD.
- Documentation discipline: Every project has architecture and system design docs with diagrams.
- Proof of execution: Deployment guides, Terraform code, monitoring, and open source contributions.
Engineering Practices
pytest with in-memory Spark fixtures and mocked Kafka/S3.
GitHub Actions for lint, test, Terraform validation, and cloud deploy.
flake8, black, type hints, and PR review discipline.
Terraform for Azure Event Hubs, Databricks, ADLS Gen2, and related Azure resources.
CloudWatch dashboards and alarms for job failures, lag, and S3 errors.
ARCHITECTURE.md, SYSTEM_DESIGN.md, DEPLOYMENT.md, and CASE_STUDY.md in every repo.
Tradeoff analysis, NFRs, scalability, and failure handling documented.
Mermaid diagrams and decision records for every project.
Architecture Gallery
Each project includes a documented architecture, design decisions, tradeoffs, failure handling, and scalability analysis.
Kafka → PySpark → Delta Lake
Design decisions: Schema validation at ingestion, watermark-based deduplication, Delta Lake append mode, checkpointed offsets.
Tradeoffs: Delta adds metadata overhead but gives ACID and time travel. Watermark bounds memory but drops very late duplicates.
Failure handling: Spark checkpoint resumes from last offset; Delta idempotent writes prevent replays from creating duplicates.
Scalability: Increase Kafka partitions and Spark executors; partition by `event_date` and `event_hour`.
Full Architecture → System Design →Cloud-Native Streaming Platform
Design decisions: Terraform modules for multi-environment parity, Azure support, managed streaming services.
Tradeoffs: Dual cloud increases module count but reduces vendor lock-in and supports more clients.
Failure handling: Terraform state locking, CloudWatch/Azure Monitor alarms, checkpointed Spark state.
Scalability: Scale partitions, Spark executors, and storage throughput independently.
Full Architecture →RAG Document QA
Design decisions: FastAPI backend, Streamlit UI, ChromaDB vector store, Ollama/OpenAI provider abstraction.
Tradeoffs: ChromaDB is local-first; Pinecone/pgvector would be used for production scale.
Failure handling: Caching reduces redundant API calls; provider abstraction allows graceful fallback.
Scalability: Document ingestion can be parallelized by chunk; vector DB is swappable.
Full Architecture →Project Case Studies
Kafka → PySpark → Delta
Real-time streaming ingestion with exactly-once semantics, schema enforcement, and cloud deployment.
Result: High-throughput data processing, comprehensive test coverage.
Read Case Study → Deployment →Cloud-Native Streaming
Multi-cloud streaming platform with Terraform, Databricks, and managed Kafka/Event Hubs.
Result: Reproducible multi-environment infrastructure.
Read Case Study → Deployment →RAG QA Chatbot
FastAPI + ChromaDB retrieval chatbot with FastAPI, Streamlit, and Ollama/OpenAI backends.
Result: 85% retrieval accuracy, 70% fewer API calls with caching.
Read Case Study → Deployment →Live Demo Evidence
The flagship pipeline has a complete deployment plan for local Docker and Databricks. The live run produces the evidence below. See LIVE_DEMO_PLAN.md for the full execution and showcase strategy.
Screenshot gallery (to be captured after live run)
Cluster status and bootstrap brokers
Live run pendingJob run success and runtime
Live run pendingDelta table and transaction log files
Live run pendingLogs and checkpoint commits
Live run pendingJob failure and lag alarms
Live run pendingSuccessful infrastructure creation
Live run pendingCI/CD workflow success
Live run pendingDelta table query preview
Live run pendingSocial Proof
Certifications
✓ Databricks Data Engineer Associate
✓ Microsoft DP-700: Implementing Data Engineering Solutions using Microsoft Fabric
✓ Google Data Analytics Professional Certificate
Published Articles
✓ Kafka → PySpark → Delta Lake guide
✓ RAG with FastAPI and ChromaDB
✓ Databricks DE Associate journey
On-portfolio; external publication in progress.
LinkedIn Content
✓ 10 technical post drafts ready
✓ 30-day posting calendar created
Posting schedule not yet executed.
Recommendations
○ Manager recommendation
○ Technical lead recommendation
○ Senior engineer recommendation
Outreach templates prepared; recommendation outreach kit coming soon.
Open Source
✓ fastify/fastify#6880 — TypeScript docs update (merged)
✓ axios/axios#11113 — README stream example fix (merged)
✓ Topicspot/skillfrisk#9 — --min-severity flag (merged)
○ trpc/trpc#7452 — secure error reporting docs (active)
○ jsdoc/jsdoc#2176 — Node.js version alignment (active)
○ xxnjms1-code/kickama-prize-lab#33 — $35 BOUNTY: coordinate auth token refresh (active)
○ axios/axios#11115 — interceptor fix review (active)
Last verified: 2026-08-01 · 3 merged / 3 active / 1 review
Conference Talks
○ No public talks yet
Targeted for Q1 2027 after open-source contributions.
Deployment Evidence
Every project has a deployment guide and infrastructure-as-code or Docker setup.
- Kafka Pipeline Deployment — local, Databricks, and Terraform.
- Cloud Terraform modules — reference infrastructure for extension.
- Cloud Platform Deployment — Azure multi-environment Terraform.
- RAG Deployment — local, Docker, and cloud ECS/EKS path.
Final Audit
Current portfolio score: ~8.2/10 after this proof page. To reach 9.0+, complete the live demo, capture the 8 screenshots, publish the articles, and execute the LinkedIn calendar. The structure, content, and plans are now in place.