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Data Engineer with less than a year in Data Analytics & Cloud Platforms
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Detail-oriented and analytical BCA graduate with hands-on experience in SQL, Microsoft Excel, Python, and data analysis. Skilled in data cleaning, validation, reporting, and transforming structured and semi-structured data to generate business insights. Familiar with Power BI, Tableau, and Google Cloud Platform for data analysis and visualization. Strong analytical, problem-solving, and communication skills with a willingness to learn new technologies.
JSS Science & Technology University (SJCE)
Bachelor of Computer Applications (BCA)
N/A – June 30, 2025
Trinity Mobility
Data Engineering Intern
January 1, 2026 – March 1, 2026
India
AI-Powered Transaction Categorization Pipeline (GCP)
June 26, 2026 – Present
Collected, cleaned,transformed, and analyzed transaction datasets using Python Loaded processed data into BigQuery for reporting and analytics Automated workflows using Apache Airflow Performed data quality validation and maintained dataset consistency
GCP Data Engineer Course
Varaahi Cloud Technologies
June 1, 2026 – Present
Java Programming
Udemy
June 1, 2026 – Present
Software Engineering
Coursework
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project and internship experience show a willingness to engage with practical data challenges and learn new technologies, which aligns with a growth-oriented culture. The diversity of tools and platforms mentioned (GCP, Apache Airflow, PySpark, BigQuery) suggests adaptability. However, the limited professional experience means cultural fit is primarily inferred from self-reported skills and project descriptions.
Soft Skills & Operational Fit
The candidate highlights analytical thinking, problem-solving, communication skills, and team collaboration, which are crucial for a data engineering role. The internship experience also mentions collaboration with cross-functional teams. Attention to detail is also listed, which is important for data quality.