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║ SYSTEM PROFILE :: NITIN.EXE ║
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║ ROLE │ Data Analyst → Data Engineer (transition in progress) ║
║ EXP │ 1.5 years analytics + actively building DE stack ║
║ STACK │ SQL · Python (PySpark/Pandas) · Airflow · dbt · Power BI ║
║ PROJECTS │ E-Commerce ETL · Retail DW · Bike Store DB · Airflow+dbt ║
║ LOCATION │ Delhi, India │ Remote-ready │ Hybrid-open ║
║ SEEKING │ DA & DE Roles │ Real problems, not toy datasets ║
║ MOTTO │ "Not just dashboards — pipelines that actually run." ║
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║ DE PIPELINE [████████████████░░░░░░░░░░░░] 70% → Full Stack Engineer ║
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#!/bin/bash
# What I'm building toward — no fluff
CURRENT_FOCUS="Production-grade ETL pipelines from scratch"
TARGET_STACK=("Airflow" "dbt" "PySpark" "Kafka" "AWS")
APPROACH="Learn by building real things, not just following tutorials"
OPEN_TO="Data Analyst & Data Engineer roles"
echo "[✔] Analyst foundations: SQL, Python, Power BI, EDA"
echo "[✔] Relational DB design: Hospital Mgmt + Bike Store DB shipped"
echo "[✔] Data warehousing: Retail DW — star schema, dimensional model"
echo "[✔] First pipeline shipped: E-Commerce Analytics (PostgreSQL → DuckDB)"
echo "[⚡] Currently: Airflow orchestration + dbt medallion transforms"
echo "[⏳] Next: Kafka streaming + cloud deployment on AWS"
echo "[🎯] End goal: Full-stack data engineer who understands the business"// CORE LANGUAGES
// DATA ENGINEERING — ACTIVE BUILD ZONE ⚡
// ANALYTICS & BI
// DATABASES & INFRA
[STATUS: SHIPPED ✔] | View Repo →
PIPELINE ARCHITECTURE:
──────────────────────────────────────────────────────────────────
Raw CSVs ──► PostgreSQL ──► Python/Pandas ──► DuckDB
(src) (ingestion) (clean+dedupe) (OLAP queries)
│
▼
Seaborn + Matplotlib
(visual storytelling)
──────────────────────────────────────────────────────────────────
What makes this real:
- Handles inconsistent, dirty real-world CSVs — not clean Kaggle data
- SQLAlchemy ORM for reliable, repeatable ingestion into PostgreSQL
- DuckDB for fast in-process analytical queries (no spinning up a warehouse)
- Full EDA with visual storytelling, not just charts for the sake of charts
[STATUS: SHIPPED ✔] | View Repo →
WAREHOUSE ARCHITECTURE:
──────────────────────────────────────────────────────────────────
Source Data ──► Staging Layer ──► Dimensional Model
(raw retail) (clean/conform) (Star Schema)
│
┌───────────────┼───────────────┐
▼ ▼ ▼
Fact Table Dim: Product Dim: Customer
(fact_sales) Dim: Store Dim: Date
──────────────────────────────────────────────────────────────────
What makes this real:
- Star schema dimensional model built for analytical query performance
- Proper fact and dimension table separation following Kimball methodology
- Slowly Changing Dimensions (SCD) handling for historical accuracy
- Designed to plug directly into BI tools like Power BI or Tableau
[STATUS: SHIPPED ✔] | View Repo →
SCHEMA DESIGN:
──────────────────────────────────────────────────────────────────
Business Requirements ──► ERD Design ──► Normalized Schema
(analysis) (entities) (3NF tables)
│
┌──────────────────┤
▼ ▼
Stored Procs Indexes
+ Triggers + Views
──────────────────────────────────────────────────────────────────
What makes this real:
- Full relational design: customers, orders, products, staff, stores, inventory
- Normalized to 3NF — no data anomalies, enforced referential integrity
- Stored procedures and triggers for business logic at the DB layer
- Window functions and JOIN-heavy queries for real analytical reporting
[STATUS: BUILDING 🔧]
PLANNED ARCHITECTURE:
──────────────────────────────────────────────────────────────────
CSV / PostgreSQL ──► Airflow DAGs ──► dbt Transforms
(sources) (orchestrate) (Bronze → Silver → Gold)
│
▼
Power BI Dashboard
(business insights)
──────────────────────────────────────────────────────────────────
Full medallion architecture. Airflow handling scheduling and orchestration, dbt handling all transformation logic with tests and documentation, Power BI at the output layer. Watch this repo.
[STATUS: QUEUED 🟡]
Kafka Producer → Kafka Topics → PySpark Streaming → PostgreSQL / Delta Lake
Event-driven pipeline with real-time processing. The step that separates analysts from engineers.
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║ SKILL ACQUISITION LOG :: 2026 ║
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║ TRACK ║ FOCUS AREAS ║ PROGRESS ║ STATUS ║
╠══════════════════════╬════════════════════════════╬══════════╬══════════════ ╣
║ Data Engineering ║ PySpark ║ ║ ║
║ ║ SQLAlchemy ║ ▓▓▓▓▓▓░░ ║ ⚡ ACTIVE ║
║ ║ Airflow ║ 75% ║ ║
║ ║ dbt ║ ║ ║
║ ║ Medallion Architecture ║ ║ ║
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║ Advanced Analytics ║ Advanced SQL ║ ║ ║
║ ║ Pandas ║ ▓▓▓▓▓▓▓▓ ║ ✅ SOLID ║
║ ║ Power BI ║ 95% ║ ║
║ ║ Tableau ║ ║ ║
║ ║ MySQL / PostgreSQL ║ ║ ║
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║ DB Design ║ Dimensional Modeling ║ ║ ║
║ ║ Star / Snowflake Schema ║ ▓▓▓▓▓▓░░ ║ ✅ SHIPPED ║
║ ║ 3NF Normalization ║ 80% ║ ║
║ ║ SCD / Slowly Changing Dims ║ ║ ║
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║ Cloud Infrastructure ║ AWS (S3, Glue, Athena, ║ ▓▓░░░░░░ ║ 🟢 SCOUTING ║
║ ║ Redshift, IAM, Lambda) ║ 25% ║ ║
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╔══════════════════════════════════════════════════════════════════════╗
║ ║
║ $ echo "Open to DA & DE roles. Let's build something real." ║
║ ║
║ > Open to DA & DE roles. Let's build something real. ║
║ ║
║ $ _ ║
║ ║
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