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Data Engineer with less than a year in Python and SQL
Results-driven Data Science and Data Engineering professional specializing in Python, SQL, Snowflake, MySQL, AWS, and ETL pipelines. Builds scalable data pipelines and applies advanced analytics to solve real-world business problems. Demonstrated understanding of Snowflake cloud data warehouse architecture, including its scalable storage, compute layers, and shared data environment. Utilized SnowSQL and Snowflake SQL to perform database operations including creation and management of databases, schemas, tables, and data loading processes. Developed and managed data ingestion workflows in Snowflake using Internal and External Stages, loading structured and semi-structured data from AWS S3 and local file systems into Snowflake tables. Built and evaluated machine learning models using supervised and unsupervised learning algorithms to solve business problems and improve predictive accuracy. Performed data analysis, preprocessing, and manipulation using Python (Pandas and NumPy) to streamline and support analytical workflows. Performed data preprocessing, feature engineering, and data normalization to improve machine learning model performance.
Swami Ramanand Tirth Marathwada University
MSc (S.E.) · Software Engineering
January 1, 2020 – January 1, 2022
Cloud-Based Sales Data Processing in Snowflake
June 21, 2026 – Present
Build an automated cloud pipeline to collect, store, and process daily sales data from multiple sources for reporting and business analytics. Collected daily sales data files from multiple sources and stored them in AWS S3. Automated ingestion into Snowflake Raw schema using Snowpipe. Transformed raw data into Staging and Business schemas using SQL. Created aggregated sales tables by region, category, and time period.
Real Estate Data Pipeline and Analytics
June 21, 2026 – Present
Scraped property listings data from Data platform and loaded it into Snowflake. Cleaned, transformed, and analyzed the data using SQL and Python, and built reports in Snowflake and Google Sheets to generate actionable insights about the property market in Poland. Scraped property listing data from Web using the Bright Data platform, automating the extraction of large-scale datasets. Loaded extracted data into Snowflake and set up databases, warehouses, tables, and stages using SnowSQL and SQL. Flattened complex JSON data in Snowflake to structured tables for downstream analysis. Transformed and cleaned datasets using SQL queries, Python scripts, and Google Sheets to prepare data for analytics. Scraped property listing data and automating the extraction of large-scale datasets.
Cultural Fit Analysis
The candidate's projects demonstrate a focus on practical, business-oriented data solutions (real estate analytics, sales data processing). The use of diverse technologies like Snowflake, AWS, Python, and SQL aligns well with modern data engineering practices. The academic background in Software Engineering further supports a structured approach to problem-solving. The projects show an ability to work independently on significant data initiatives, which is a positive indicator for cultural fit in roles requiring self-starters. However, the lack of team-based project descriptions or collaborative experiences makes it difficult to assess team collaboration aspects fully.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on end-to-end data pipeline development, from data extraction to reporting. The focus on automation and scalable solutions suggests an operational mindset. However, without direct assessment data, specific soft skills like teamwork, problem-solving under pressure, or communication style cannot be definitively evaluated.