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Data Engineer with less than a year in ETL pipeline development & data analysis.
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Data professional with 7 months of internship experience in Data Engineering and strong analytical skills. Hands-on experience in SQL, Python, Azure Data Factory (ADF), ETL pipeline development, data cleaning, transformation, and reporting. Experienced in working with structured datasets, data validation, and cloud-based data solutions. Skilled in Power BI, Excel, and exploratory data analysis. Seeking opportunities in Data Analyst and Data Engineer roles where I can contribute to data-driven decision making and scalable data solutions.
Parvathareddy Babul Reddy Visvodaya Engineering College
B.Tech · Electronics and Communication Engineering
January 1, 2020 – January 1, 2025
Wallero Technologies
Data Engineer Intern
November 1, 2025 – May 1, 2026
Hyderābād, Telangana, India
End-to-End ETL Pipeline Using Azure Data Factory
June 1, 2026 – Present
Built an end-to-end ETL pipeline to extract data from CSV/Excel files, transform it, and load it into an Azure SQL Database using Azure Data Factory. Key Highlights: Designed ADF pipelines with Copy Activity and Mapping Data Flows. Performed data cleaning and transformations (null handling, data type conversion). Implemented parameterization for dynamic file handling. Scheduled pipelines using ADF triggers. Validated data accuracy using SQL queries.
Data Cleaning & Transformation Using SQL
June 1, 2026 – Present
Processed raw transactional data to create clean, analytics-ready tables using SQL. Key Highlights: Removed duplicates and handled missing values. Created fact and dimension tables. Wrote optimized SQL queries and joins. Implemented basic data quality checks. Improved query performance using indexing (basic level).
Automated File Ingestion Pipeline (ADF)
June 1, 2026 – Present
Developed an automated pipeline to ingest multiple files from a source folder and load them into separate target tables. Key Highlights: Used ForEach activity in ADF to process multiple files. Implemented dynamic file naming and folder paths. Logged pipeline success and failure records. Handled schema validation and file format consistency.
Python-Based Data Transformation Pipeline
June 1, 2026 – Present
Used Python (Pandas) to clean, transform, and prepare raw datasets before loading them into a database. Key Highlights: Performed data cleaning, filtering, and aggregation. Automated transformation scripts. Loaded processed data into SQL tables. Generated summary reports for validation.
Data Warehouse Design (Mini Project)
June 1, 2026 – Present
Designed a basic data warehouse schema for sales data. Key Highlights: Created Star Schema with fact and dimension tables. Defined grain and business metrics. Loaded data using ETL logic. Queried data for reporting use cases.
Python Programming
Future Skills Prime, NASSCOM
June 1, 2026 – Present
Data Analyst Certification
MSME
June 1, 2026 – Present
Achieved a perfect score (100%) on the assessment, indicating comprehensive knowledge and proficiency in Azure Data Engineering, including Databricks DLT, streaming, and cost optimization.
Strengths
Limitations
Cultural Fit Analysis
The candidate's project diversity, ranging from SQL-based data cleaning to Azure-based ETL pipelines and data warehouse design, shows a broad interest in data engineering domains. The volunteer experience indicates a socially conscious individual. The target role of Data Engineer aligns well with the candidate's technical skills and internship experience, suggesting a good fit for a role focused on building and maintaining data infrastructure.
Soft Skills & Operational Fit
The candidate's professional summary and project descriptions indicate a proactive approach to learning and problem-solving. The internship experience highlights collaboration and troubleshooting skills. The volunteer experience suggests a commitment to community and ability to teach, which can translate to good communication and mentorship potential within a team.
Achieved a perfect score (100%) on the assessment, demonstrating excellent understanding of fundamental data engineering principles and data platforms.
Strengths
Limitations