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Data Engineer with less than a year in SQL & Python.
Currently pursuing a Bachelor of Technology in Production and Industrial Engineering, I am an aspiring Data Engineer with a strong foundation in SQL, Python, and data pipeline orchestration tools like Apache Airflow. My internship as a Data Analyst and various projects in data engineering and warehousing demonstrate my ability to conduct end-to-end data analysis, build automated ELT pipelines, and design robust data architectures. I am passionate about leveraging data to drive actionable insights and build production-grade solutions.
Indian Institute of Technology Roorkee
B.Tech · Production and Industrial
August 1, 2021 – June 30, 2025
Happy Model School, Varanasi
CBSE (X)
N/A – May 31, 2019
Central Hindu Boys School BHU School, Varanasi
CBSE (XII)
N/A – May 31, 2021
CodeAlpha
Data Analyst Intern
May 1, 2026 – June 1, 2026
India
Automated Weather Data Engineering Platform
June 1, 2026 – June 1, 2026
Engineered a fully automated hourly ELT pipeline ingesting weather data for 10 Indian cities via OpenWeatherMap API built a 5-task Airflow DAG with parallel extraction, XCom quality gates, and exponential-backoff retry; landed raw JSON to a Hive-partitioned local data lake (city/year/month/day/hour) and batch-loaded 9,000 rows/day into Google BigQuery using the Jobs API (free-tier compatible, replacing streaming inserts). Implemented a medallion-style dbt transformation layer with 6 SQL models across 3 BigQuery datasets - staging views (type-cast, rename, null-coalesce), an intermediate join model (latest current conditions - 24h forecast by city), and 3 mart tables (daily aggregates, city comparison with temperature ranking, 24h forecast) with day partitioning + city clustering, 20 automated data tests, and a custom generic test macro serving a live Looker Studio dashboard. Delivered a full observability and reliability layer: a pipeline_health_check.py script performing 7-stage data lineage checks (raw files → BQ raw → stg → mart) with per-date city-level gap detection and auto-generated fix commands; a 4-job GitHub Actions CI pipeline (lint + 13 unit tests + dbt compile + Docker validate) on every push; - production-grade reliability patterns on a zero-cost infrastructure stack.
View ProjectEnd-to-End Data Warehouse
January 1, 2026 – February 1, 2026
Design a 3-tier medallion architecture (Bronze/Silver/Gold) on SQL Server, consolidating 6+ raw CSV datasets from 2 source systems (ERP & CRM) with end-to-end ETL pipelines - reducing data inconsistencies to zero before loading into the Silver layer. Engineered a star schema with 1 fact table and 4+ dimension tables, enabling optimized analytical querying across customer behavior, product performance, and sales trends. Delivered a multi-page interactive dashboard with 5+ KPI cards, trend line charts, categorylevel drill-downs, and dynamic slicers, translating warehouse data into actionable business insights for strategic decision-making. — GitHub (PowerBI)
View ProjectAdvanced Valuation and Strategy - M&A, Private Equity, and Venture Capital
Erasmus University Rotterdam
June 1, 2026 – Present
Intermediate SQL
HackerRank
June 1, 2026 – Present
Wealth and Personal Banking Virtual Experience
HSBC
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and initiative in data engineering, aligning well with a target role in this field. The diversity of projects, from weather data ELT to a general data warehouse, shows a breadth of application. However, the experience is primarily academic and personal projects, with only one brief internship as a Data Analyst. This indicates a strong learning curve and self-starter attitude, which can be a good cultural fit for dynamic environments, but also suggests a need for mentorship in a professional setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on delivering robust, production-grade solutions, even in personal projects. The emphasis on observability, reliability, and automated testing suggests a strong operational mindset. While direct team collaboration experience in data engineering is limited to personal projects, the detailed project descriptions imply an organized and thorough work ethic.