Hi 👋, I'm Ubaid

👨‍💻 About Me

I'm a Data Engineer in the making, currently working as an Associate Analyst at AML RightSource (AMLRS), where I work hands-on with financial data every day — cleaning, reconciling, filtering, and analyzing customer records with Excel and SQL to support accurate reporting and data review.

My path into data started with full-stack web development. I shipped production features as a developer intern at two digital studios (MERN, React, React Native, HonoJS) and spent 1.5+ years teaching web development to international students on Preply (165+ classes, 4.3/5★ rating). That engineering foundation — APIs, databases, clean code, and relentless debugging — is exactly what I'm now channeling into data engineering.

Today I build end-to-end pipelines with Python, SQL, PySpark, dbt, Apache Airflow, Kafka, and Databricks, including a lakehouse project that ingests raw data with PySpark Structured Streaming and models it through a Medallion (Bronze → Silver → Gold) architecture with dbt.

• 🔭 Currently building: end-to-end ETL/ELT pipelines on Databricks (PySpark + dbt + Delta)
• 🎯 Career goal: become a Data Engineer who turns raw, messy data into reliable, analytics-ready datasets
• 🌱 Currently learning: streaming architectures, workflow orchestration at scale, and lakehouse design patterns
• 🧠 What I bring from my dev years: API design, data modeling, and performance optimization — now applied to data platforms
• ⚡ Fun fact: I've debugged more web apps than I can count — now I debug data pipelines instead

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💼 Experience

📊 Associate Analyst — AML RightSource (AMLRS)

📍 Noida, India · 🗓️ July 2026 – Present

• Process, organize, filter, and analyze customer financial records and customer information using Microsoft Excel to support accurate reporting and data review.
• Retrieve, match, and reconcile information across datasets using VLOOKUP, XLOOKUP, and HLOOKUP to identify corresponding customer records.
• Apply advanced filters and conditional logic to surface relevant records, investigate data conditions, and support accurate reporting.
• Use Pivot Tables to summarize and analyze customer data, identify patterns, and generate structured views for reporting and review.
• Support data preparation and reporting activities using Excel and SQL-based data handling techniques.

🎓 Web Developer Tutor — Preply

📍 Remote · 🗓️ Dec 2024 – Jun 2026

• Taught full-stack web development — frontend, backend, and the MERN stack — to 10+ international students across 165+ one-on-one classes with a 4.3/5★ rating.
• Designed a learn-by-building teaching style: master the basics → solve practice problems → ship small projects → repeat the pattern.
• Deepened my own understanding of HTML, CSS, JavaScript, React, Node.js, and databases by teaching them daily.

💻 Full Stack Developer Intern — Impresio Studio

📍 Remote · 🗓️ Jan 2026 – Mar 2026

• Built full-stack features on a production codebase using the MERN stack.
• Designed MongoDB schemas, integrated REST APIs, and optimized backend services.
• Contributed to production-ready code, debugging, and performance optimization.

💻 Full Stack Developer Intern — Pixelwand Studio

📍 Onsite · 🗓️ Jul 2025 – Dec 2025

• Built full-stack features using ReactJS, React Native, and HonoJS for backend API development.
• Designed MongoDB schemas, integrated REST APIs, and optimized backend services.
• Shipped production-ready code with a strong focus on debugging and performance.

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🎓 Education

Bachelor of Computer Applications (BCA) · TMU (Teerthanker Mahaveer University), Moradabad — Class of 2026

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🛠️ Tech Stack

Languages & Core

Analytics & BI

Python for Data

Databases

Data Engineering

Cloud & DevOps

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🚀 Featured Data Engineering Projects

🏗️ Databricks + PySpark + dbt — End-to-End Pipeline

An end-to-end data engineering pipeline on Databricks: ingests raw CSVs with PySpark Structured Streaming, transforms them through a Medallion (Bronze → Silver → Gold) architecture using dbt, and delivers analytics-ready Gold tables.

Databricks · PySpark · dbt · Delta · SQL

📈 Vendor Performance Analysis

A complete pipeline from raw CSV ingestion to interactive dashboards — ETL, exploratory data analysis, statistical hypothesis testing, and business recommendations on vendor purchasing, sales, and profitability.

Python · Pandas · NumPy · SQL · SciPy · Power BI

🛒 Customer Shopping Behavior Analysis

Analyzed 3,900+ purchases to uncover spending patterns, customer segments, product preferences, and subscription behavior — served as SQL-backed Power BI dashboards.

Python · Pandas · SQL · PyMySQL · Power BI

🚚 Supply Chain Delivery Analysis

Root-caused chronic late deliveries across 172,765 orders and quantified their financial impact, delivering data-backed recommendations to improve logistics performance.

Python · Pandas · Seaborn · EDA

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🧩 Freelance & Full-Stack Highlights

• 🌴 Lionyard — Full-stack real-estate platform for a Dubai property company: marketing site, role-based agent/admin dashboard, and Node.js REST API covering the complete property lifecycle. React Express MongoDB JWT
• 🧾 BillRest — Modern billing & inventory management software (General + Health segments) handling billing, invoicing, appointments, and inventory. MERN TypeScript
• 🏍️ NoLimitBikers — Course-selling web app for bike learners in the Netherlands. Next.js Tailwind CSS

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📈 GitHub Stats

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🐍 Contribution Snake

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🤝 Let's Connect

I'm always open to Data Engineering opportunities, collaborations, and interesting data problems. Feel free to reach out — I reply fast!

“Turning raw data into reliable, decision-ready pipelines — one ETL job at a time.” 🚀

IMPORTANT DE TERMS - The following data engineering terms are explained in the video:

• Database (1:55)
• ACID Properties (3:44)
• Write Ahead Log (WAL) (11:07)
• Data Warehouse (14:30)
• Data Lake (19:54)
• Data Lakehouse (27:34)
• Delta Lake / Open Table Format (33:14)
• Lakehouse / Lakebase (36:14)
• Data Mart (42:01)
• Data Mesh (46:40)
• Data Fabric (59:57)
• ETL / ELT (103:54)
• Incremental Data Loading (110:13)
• Back-date Refresh (111:08)
• Idempotency (118:13)
• Upsert (118:33)
• Database Modeling (OLTP vs OLAP) (121:33)
• Normalization (126:38)
• Fact and Dimension Tables (131:42)
• Star Schema (134:29)
• Snowflake Schema (134:44)
• Slowly Changing Dimensions (SCD) (136:34)
• Batch vs. Streaming Processing (153:14)
• Lambda Architecture (153:30)
• Kappa Architecture (202:48)
• CDC (Change Data Capture) (203:40)
• Change Tracking (CT) (206:40)
• Schema Evolution (212:37)
• Row-based vs. Column-based File Formats (214:06)
• HDFS (Hadoop Distributed File System) (223:49)
• Apache Spark (237:18)
• Data Orchestration (238:18)
• dbt (data build tool) (243:52)
• Apache Kafka (249:28)
• CI/CD / DataOps (252:44)