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Data Engineer with less than a year in ETL, Data Modeling & SQL
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B.Sc. Computer Science & IT graduate (May 2025, CGPA 8.94) who self-taught data engineering outside of university. With no formal training in the field, I independently studied ETL workflows, data modeling, and pipeline development and applied that knowledge by building three end-to-end data pipelines using real public datasets, Python, and PostgreSQL. I am now pursuing an M.Sc. in Artificial Intelligence & Machine Learning at IGNOU (starting June 2026) to deepen my technical foundation further. I am familiar with Azure Data Factory, Databricks, and PySpark through self-study, though my hands-on cloud experience is still growing. I am actively looking for an internship, trainee, or associate-level data engineering role where I can be mentored, contribute from day one, and grow steadily in the field.
Indira Gandhi National Open University (IGNOU)
M.Sc. · Artificial Intelligence & Machine Learning
June 1, 2026 – June 30, 2028
Silver Oak University, Ahmedabad
B.Sc. · Computer Science & Information Technology
August 1, 2022 – June 30, 2025
Maharshi Gurukul, Halvad
HSC · Commerce
June 1, 2021 – May 31, 2022
Nourish Connect - Full Stack Web Application
June 1, 2026 – Present
Built a MERN stack platform connecting food donors with NGOs, with backend REST APIs and a dashboard for donation tracking and operational reporting.
Uber Trip Data ETL Pipeline
June 1, 2026 – Present
Processed ~100,000 Uber trip records from a public dataset through a complete ETL workflow extract, clean, transform, and load into PostgreSQL. Designed a warehouse schema with a rides fact table plus vendor and time dimension tables, applying dimensional modeling concepts learned through self-study. Wrote Python scripts for data cleaning, type conversions, trip duration calculations, and daily revenue aggregations. Added structured logging and exception handling to make the pipeline observable and easier to debug.
Retail Sales Data ETL Pipeline
June 1, 2026 – Present
Built an ETL pipeline for multi-store retail transaction data, designing a star schema with sales fact, product, store, and customer dimension tables. Handled data validation, deduplication, and aggregation to calculate store-level KPIs such as total revenue and average order value. Created charts using Matplotlib and Seaborn to visualize sales trends and compare store performance.
Flight & Airport Data ETL Pipeline
June 1, 2026 – Present
Integrated airlines, airports, and flight routes datasets fetched via REST API into a normalized PostgreSQL warehouse. Performed dataset merging, deduplication, and data cleaning to ensure consistency across three source files. Enabled SQL-based analysis on airline route coverage and airport connectivity.
Data Visualization: Empowering Business with Effective Insights
Tata Group / Forage Job Simulation
June 1, 2026 – Present
Python 101 for Data Science
IBM
June 1, 2026 – Present
AI & Generative AI Certification Program (In Progress)
Kaushalya The Skill University
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's proactive approach to learning and project development, despite lacking formal experience, aligns well with a culture that values initiative and continuous improvement. The focus on personal projects to gain practical skills demonstrates a strong drive. The pursuit of an M.Sc. in AI/ML further indicates a commitment to deepening technical knowledge. The candidate is explicitly looking for an internship/trainee role, which suggests an understanding of their current experience level and a desire to grow within a team.
Soft Skills & Operational Fit
The candidate exhibits strong self-learning capabilities and initiative, as evidenced by independently building multiple ETL pipelines. The stated 'genuine curiosity about cloud data platforms' and readiness to be mentored suggest a positive attitude towards continuous learning and team collaboration. The structured logging and exception handling in the Uber Trip Data ETL Pipeline project indicate an understanding of operational best practices for data pipelines.