Start with the question
I break a broad problem into measurable questions first—such as demand, pay, volume, or spending—before writing the query that answers it.
SQL analysis · joins · filters · aggregations
Hi 👋
G. Rohith Reddy
Aspiring Data Engineer | CSE–AI Student
Turning raw data into useful answers.
Open to data engineering internships
I’m a SQL builder
I’m a B.Tech student at Marwadi University focused on the work behind trustworthy data: clear SQL, useful transformations, and pipelines that turn raw information into decisions. I’m building that foundation through hands-on projects, practical certifications, and consistent learning.
3 data projects6 practical certificates4 semesters tracked
The proof
What I bring to the table
I’m G. Rohith Reddy, a B.Tech student in Computer Science and Engineering (Artificial Intelligence) at Marwadi University. My goal is to become a Data Engineer.
I enjoy working with SQL and databases, and I’m building my skills in Python and data engineering through projects on GitHub, practical courses and certificates.
- 7.25CGPA after 4 semesters (now in 5th)
- 3Data projects on GitHub
- 6Certificates from Oracle, Cisco, AWS and CII
How I work
From questions to useful data
Make messy data useful
My ETL project focuses on the practical middle layer: taking raw sales records, cleaning them with Python and pandas, loading them into SQLite, and summarising them with SQL.
Ingestion · transformation · analytical summaries
Keep learning in public
I document projects on GitHub and reinforce the fundamentals through coursework and certificates across SQL, cloud foundations, Linux, Java, Python, and applied AI.
Documentation · practice · steady improvement
Selected work
Projects built to make data useful
Featured build · end-to-end warehouse + marts
Data Warehouse & Mart Build: End-to-End ETL Pipeline
- Purpose
- Transform raw job-posting CSVs from Google Cloud Storage into a normalized star-schema warehouse, then create specialized analytical marts for fast, focused questions.
- Architecture
- A DuckDB pipeline moves from extraction and loading into company_dim, skills_dim, job_postings_fact and bridge tables, then branches into flat, skills and priority marts.
- Technical evidence
- Wrote explicit table-creation and loading scripts, defined the grain of the fact table, used explicit data types and record-count checks, and kept reruns safe with DROP TABLE IF EXISTS.
- SQL techniques
- Uses CTEs, DATE_TRUNC, EXTRACT, STRING_AGG, explicit column mapping and MERGE-based incremental updates for the priority mart.
- Tools
- DuckDB with httpfs, SQL, Google Cloud Storage, VS Code, Terminal, Git and GitHub
- Outcome
- A single source of truth for analytical queries plus marts optimized for ad-hoc exploration, skill-demand time series and priority-role tracking.
Data Jobs Analysis (SQL Data Engineering Projects)
- Purpose
- Explore the Data Engineer job market with SQL: which skills are most in demand, which roles pay the most, and which skills balance salary and demand best.
- What I did
- Wrote and documented SQL queries on skill demand, median salary and a custom demand-and-pay score for remote Data Engineer postings.
- Tools
- SQL, DuckDB, Visual Studio Code, Git and GitHub
- SQL operations
- Joins across multiple tables, COUNT() and MEDIAN() aggregations, and filtering and sorting with WHERE, HAVING and ORDER BY.
- Outcome
- A documented, reproducible analysis that turns job-market data into practical questions about demand, compensation, and skill choices. In the recorded output, SQL (29,221 postings) and Python (28,776) were the most requested skills.
Python + SQL · extract, transform, load, analyze
Automated Sales ETL Pipeline
- Purpose
- Show the complete path from messy input data to a queryable analytical result, in a small project that is easy to check by hand.
- Pipeline
- Extract the raw CSV with pandas, Transform it (missing customer names become “Unknown”, order status is trimmed and capitalised, and Total_Amount = Quantity × Price_Per_Unit), Load it into a SQLite table called cleaned_sales, then Analyze with a SQL GROUP BY on status.
- Verified output
- On the sample data it reports 1 cancelled order (1,600), 2 delivered (1,750), 1 pending (900) and 1 shipped (1,200).
- Tools
- Python, pandas, SQLite, SQL
- Scope
- A deliberately small build on 5 sample records, made to learn the ETL pattern end to end. Re-running it replaces the table, so results stay reproducible.
Skills
Technical skills
Programming
- Java working knowledge
- C working knowledge
- C++ working knowledge
- Python (pandas) learning
Databases and data
- SQL practising
- DuckDB practising
Data engineering
- ETL pipelines learning
- Star-schema warehouse practising
- Data cleaning and transformation practising
- Analytical data marts practising
Tools
- Git and GitHub using
- Visual Studio Code using
Basics
- Linux basics
- AWS cloud basics
- Machine learning basics
Education
Education
B.Tech, CSE (Artificial Intelligence)
Marwadi University, Rajkot · 2024 – 2028 (expected)
CGPA 7.25 after 4 semesters · currently in 5th semester
Intermediate (Class XII)
Vamsi Junior College, Srikalahasti · 2024 · 80.9%
Secondary School (Class X)
Bhashyam High School, Srikalahasti · 2022 · 86.8%
Relevant coursework: Database Management Systems, Data Structures, Programming with Python, Java Programming, Machine Learning Essentials, Operating System and Virtualization, Probability and Statistics.
B.Tech SGPA by semester
6.84
7.00
7.60
7.52
Sem 1Sem 2Sem 3Sem 4
Credentials
Proof of consistent learning
Select a certificate to view it.
Database Programming with SQLOracle Academy · Nov 2025
Applied Machine Learning and AI (Foundational Course)Confederation of Indian Industry (CII) · Sep 2026
AWS Academy Graduate: Cloud Foundations (20 hours)AWS Academy · 2026
Java FundamentalsOracle Academy · Apr 2026
Linux EssentialsCisco Networking Academy · Mar 2026
Python using AI WorkshopAI for Techies · Aug 2025
Achievements
Achievements and interests
Hosted the expert session “RAG from Scratch”
Hosted an expert session at Marwadi University in October 2026 featuring Ms Abhilasha Mangal, Senior Data Scientist at IBM, with 1,500 students attending. I supported event coordination and technical communication, which strengthened my presentation skills and my ability to hold an audience’s attention.
Interests: music and old-school gym training.
Next step
Let’s build something useful
I’m open to internship and entry-level data engineering opportunities where I can contribute with SQL, learn from experienced engineers, and keep turning messy data into clear outputs. The best way to reach me is by email.
gundarohithreddy@gmail.com