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Data Engineer with 1+ years in Python, SQL & ETL/ELT pipelines.
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Aspiring Data Engineer with strong foundations in Python, SQL, and ETL/ELT pipelines. Experienced in data modeling, data cleaning, and transformation workflows. Eager to build scalable data ingestion pipelines, work with cloud data warehouses, and contribute to modern lakehouse architectures. Familiar with dbt concepts, AWS fundamentals, and API integration. Collaborative, ownership-driven, and passionate about building robust data platforms that power business insights.
Global Academy of Technology, Bengaluru
Bachelor of Engineering · Information Science & Engineering
July 1, 2022 – June 1, 2026
Deloitte
Data Analytics Virtual Internship
March 1, 2025 – June 1, 2026
India
End-to-End ELT Data Pipeline (Portfolio Project)
January 1, 2026 – June 1, 2026
Designed and built a data ingestion pipeline that extracts data from a public REST API, loads raw JSON into AWS S3, and transforms it into structured Silver/Gold layer tables using dbt models. Implemented data quality checks using dbt tests (not_null, unique, accepted_values) to validate pipeline outputs at each transformation stage. Modeled data using star schema principles with clearly defined fact and dimension tables for downstream analytics consumption. Versioned all pipeline code and dbt models using Git; documented transformation logic and lineage using dbt docs.
Uber Rides Data Ingestion & Transformation Pipeline
January 1, 2026 – June 1, 2026
Built an automated data ingestion script that loaded raw Uber ride CSVs into a PostgreSQL database, simulating a marketplace data source pipeline. Wrote modular Python functions for data cleaning, datetime feature engineering, and deduplication — reusable across multiple pipeline runs. Authored SQL transformation queries to aggregate ride demand by time, purpose, and distance — equivalent to Silver/Gold layer logic in a modern ELT stack. Validated pipeline outputs with row count checks and null-value assertions to ensure data integrity end-to-end.
Customer Segmentation Data Model
January 1, 2026 – June 1, 2026
Designed and implemented a data model to structure retail transaction data for customer analytics, applying PCA for dimensionality reduction prior to clustering. Used K-Means and Hierarchical Clustering to segment customers into distinct groups based on purchasing behaviour; stored results back to a PostgreSQL table for downstream use. Built a clean data transformation pipeline using Pandas for feature engineering before loading modeled outputs to the database.
Olympics Analytics Dashboard (1896–2024)
January 1, 2026 – June 1, 2026
Designed a Power BI dashboard fed by a Python-based data preparation pipeline that standardized, cleaned, and merged historical Olympics datasets spanning 128 years. Implemented data model relationships in Power BI (star schema) connecting athlete, country, event, and medal fact tables for multi-dimensional analysis. Enabled real-time interactivity with slicers for country, region, gender, and year — allowing users to explore complete country performance trends across all Olympic games.
Database Management & SQL
Udemy
June 1, 2026 – Present
GenAl for Everyone
Coursera
June 1, 2026 – Present
Artificial Intelligence Fundamentals
IBM Skills Build
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a data-driven organization, showing initiative through diverse personal projects covering various aspects of data engineering and analytics. The focus on building robust data platforms and contributing to business insights aligns well with a growth-oriented, impact-driven culture. The breadth of skills and tools used across projects indicates adaptability and a willingness to learn new technologies.
Soft Skills & Operational Fit
The candidate's resume highlights a 'collaborative, ownership-driven, and passionate' attitude, which suggests a good operational fit for team environments. The project descriptions indicate an ability to work independently on end-to-end solutions and a focus on data integrity and documentation, aligning with best practices in data engineering.