I build data and AI products like a founder: start from a painful user problem, find the workflow that creates leverage, then engineer the data layer, product loop, and trust system behind it.
My core edge is the blend of senior data engineering, product analytics, and career-tech product building. I have worked across retail, ecommerce, and supply-chain analytics, and I now use that operator mindset to build systems that help people make better decisions, faster.
Business problem -> Data model -> Quality layer -> Product workflow -> Measurable outcome
flowchart LR
A["Founder Lens<br/>PrepnPlaced.com"] --> B["CareerOS<br/>AI career intelligence"]
A --> C["LinkedIn Growth Studio<br/>Content operations"]
A --> D["Analytics Portfolio<br/>Snowflake + dbt"]
B --> B1["Resume parsing<br/>ATS scoring<br/>Interview workflows"]
C --> C1["Idea generation<br/>Quality gates<br/>Official API publishing"]
D --> D1["Raw ingestion<br/>Staging models<br/>Marts + dashboards"]
B1 --> E["User outcomes"]
C1 --> E
D1 --> E
E --> F["Career clarity<br/>Trusted decisions<br/>Measurable growth"]
style A fill:#0F172A,color:#fff,stroke:#0EA5E9,stroke-width:2px
style B fill:#E0F2FE,stroke:#0284C7,color:#0F172A
style C fill:#DCFCE7,stroke:#16A34A,color:#0F172A
style D fill:#F8FAFC,stroke:#64748B,color:#0F172A
style F fill:#0F172A,color:#fff,stroke:#22C55E,stroke-width:2px
| Product / Area | What I am building |
|---|---|
| PrepnPlaced.com | Career preparation platform for tech learners and working professionals |
| CareerOS | AI career intelligence system for resumes, interviews, job search, and guided career execution |
| LinkedIn Growth Studio | Private content operations system for compliant AI-assisted LinkedIn publishing |
| Analytics Engineering Portfolio | Public Snowflake, dbt, dashboarding, and data quality projects |
| Founder Lens | I care about user pain, activation, retention, workflows, and business outcomes, not just shipping code. |
| Data Engineering Lens | I design reliable pipelines, lakehouse layers, quality checks, and analytics-ready data models. |
| Analytics Lens | I turn raw operational data into KPIs, KRIs, root-cause insights, and decision dashboards. |
| AI Product Lens | I build LLM-assisted workflows for resume parsing, ATS scoring, content generation, and career intelligence. |
| Outcome | Result |
|---|---|
| Outstanding vendor payments reduced | 14% |
| Reconciliation errors reduced | 20% |
| Ecommerce return rates reduced | 15% |
| Shipping costs reduced | 12% |
| Critical data availability improved | 17% |
| Near real-time reporting enabled | 90% coverage |
| New pipeline development effort reduced | 30% |
| Resume optimization manual effort reduced | 80% |
AI-powered career intelligence platform for resume scoring, optimization, interview preparation, offer intelligence, live mock interviews, and supervised job-search workflows.
| Layer | Capability |
|---|---|
| Resume Intelligence | PDF/DOCX parsing, ATS scoring, keyword matching, role-fit analysis |
| AI Optimization | LLM-assisted rewriting, bullet improvement, summary refinement |
| Interview System | Live mock interviews, weakness analytics, readiness roadmap |
| Career Workflow | Application tracking, salary intelligence, recommendations |
| Platform | Next.js, FastAPI, PostgreSQL, Redis, Gemini, WebSockets, RAG-ready architecture |
How I think about CareerOS
CareerOS is not just a resume tool. The larger product idea is a career operating system: one place where a candidate can understand where they stand, improve their profile, prepare for interviews, and execute a focused job-search workflow.
The product loop is simple:
- Diagnose the candidate profile.
- Score resume and role fit.
- Identify the most important gaps.
- Generate better resume and interview assets.
- Track applications, readiness, and next actions.
- Use data feedback to improve the next iteration.
| Project | Stack | What it proves |
|---|---|---|
| DBT Project Pipeline | Snowflake, dbt, SQL, Python | Retail sales pipeline with raw ingestion, staging, marts, tests, and public-safe config |
| Snowflake DBT Project | Snowflake, dbt, Streamlit, Python | Inside Airbnb analytics project with staging/intermediate/mart layers, data tests, docs, and dashboard |
| Domain | Tools |
|---|---|
| Data Engineering | PySpark, Spark, Databricks, Delta Lake, Medallion Architecture, ETL/ELT, Airflow |
| Analytics Engineering | dbt, Snowflake, PostgreSQL, SQL Server, Oracle SQL, MySQL, data modeling |
| BI And Analytics | Power BI, Tableau, DOMO, Excel, Python Dash, Plotly, KPI/KRI dashboards |
| AI Products | LLM applications, prompt engineering, resume parsing, NLP, ATS scoring, document workflows |
| Cloud And DevOps | Azure Data Factory, Azure Data Lake, AWS Glue, GCP, Docker, Git, CI/CD concepts |
| Role | Organization | Focus |
|---|---|---|
| Senior Data Engineer / Data Engineer II | 7-Eleven | Databricks, PySpark, lakehouse architecture, supply-chain analytics, data quality |
| Product Analyst | Target | Ecommerce analytics, KPI/KRI dashboards, SQL optimization, product insights |
| SE Data Analyst | Coforge | Retail analytics, KPI reporting, stakeholder decision support |
- Databricks Lakehouse Fundamentals / Spark Certification
- Microsoft Azure Data Engineer Associate (DP-203)
- AWS Certified Data Analytics
- Google Certified Data Analyst
- Building products that create visible user outcomes
- Designing data systems that are explainable, monitored, and trusted
- Turning analytics from reporting into product leverage
- Helping learners and professionals move from confusion to career clarity
I am open to conversations around data engineering, analytics platforms, AI career-tech products, and founder-led product building.
