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Data Analyst with less than a year in SQL, Python & AWS for Data Analysis and Engineering
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am a fresher IT professional with a strong focus on Data Analysis and Data Engineering. Skilled in Apache Spark, AWS, Python, and SQL, I have a solid foundation in analyzing, transforming, and interpreting data to generate meaningful business insights and support data-driven decision-making. I am seeking a Data Analyst or Data Engineer role where I can leverage my analytical and technical skills to contribute to organizational growth and success.
Sant Gadge Baba University, Amravati, Maharashtra
Bachelor of Engineering · Computer Science Engineering
August 1, 2019 – June 30, 2023
Maharashtra State Board
Higher School Certificate
June 1, 2018 – May 31, 2019
Maharashtra State Board
Secondary School Certificate
June 1, 2016 – May 31, 2017
Sales Performance Analysis
June 23, 2026 – Present
Collected sales data from multiple teams and cleaned it using Python to remove inconsistencies, missing values and duplicates before analysis. Wrote SQL queries to analyse monthly sales trends, product-wise revenue and regional performance across different business units. Built an interactive Power BI dashboard showing key metrics like revenue growth, top products and team-wise performance to help management track results weekly. Shared findings with the business team through a structured report highlighting underperforming areas and suggested improvements that helped increase quarterly sales.
Customer Retention Analysis
June 23, 2026 – Present
Analysed customer data to identify patterns in customer drop-off by examining purchase frequency, subscription duration and payment history using SQL and Python. Segmented customers into active, at-risk and churned groups based on behaviour data and created a summary report highlighting which segment contributed most to revenue loss. Designed a Power BI dashboard to track monthly retention rates and customer segment trends, giving the business team a clear view of retention health over time. Presented key findings to stakeholders with clear visuals and recommendations on re-engagement strategies which helped the team plan targeted campaigns.
Configuration Based Data Pipeline in AWS
June 23, 2026 – Present
Developed a three-layered AWS data pipeline architecture to ingest raw data from multiple sources into Amazon S3, process it through structured cleansing and transformation stages, and load the final output into a harmonized bucket for downstream analytics consumption. The solution replaced a legacy ETL tool, successfully migrating 42 use cases to a fully AWS-native stack. Designed and implemented the end-to-end pipeline using AWS Lambda and AWS Glue Jobs to pull data from diverse source systems into the raw S3 bucket via Python-based ingestion scripts. Executed business-specific data quality checks on raw data, including null handling, file size validation, header integrity checks, and schema standardization, stored cleansed output in Parquet format for optimized columnar querying. Applied PySpark-based transformation logic within AWS Glue ETL jobs on the cleansed dataset, performing business rule driven aggregations and restructuring before loading results into the harmonized S3 bucket. Orchestrated the full pipeline execution using AWS Step Functions, ensuring reliable sequencing of ingestion, cleansing, and harmonization stages with built-in error handling and retry mechanisms. Configured fine-grained AWS IAM roles following the principle of least-privilege access for all pipeline components; implemented centralized monitoring and alerting using AWS CloudWatch logs and metric filters. Developed reusable Python utility functions for data validations and standardizations, covering file structure integrity, data type enforcement, and domain-specific business rules.
Cultural Fit Analysis
The candidate's projects demonstrate a good breadth of skills relevant to both Data Analysis and Data Engineering, which aligns with the target role's requirements. The diversity of projects (customer retention, sales analysis, data pipeline) indicates adaptability and a willingness to tackle different types of data challenges. The focus on AWS-native solutions suggests an interest in modern cloud technologies. However, as a fresher with no professional experience, the cultural fit is primarily inferred from project descriptions and technical skill alignment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work through data analysis problems from collection to presentation, suggesting problem-solving and communication skills. The AWS data pipeline project shows an understanding of structured development and operational considerations like error handling and monitoring. However, without specific psychometric or English test scores, a detailed assessment of soft skills, stress handling, and team collaboration is not possible.