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Data Engineer | SQL, Python, PySpark, Azure Data Factory, Databricks | ETL/ELT Pipelines | Data Warehousing | Big Data & Analytics | Data Modeling | Building Scalable Data Solutions
Data Engineer with hands-on experience designing and developing batch ETL/ELT pipelines for large-scale structured and semi-structured data processing using Python, SQL, PySpark, and Pandas. Skilled in building scalable data workflows, data transformation, data validation, query optimization, and performance tuning for analytics and reporting use cases. Strong understanding of data warehousing concepts including Fact-Dimension Modeling, Star Schema, and Snowflake Schema along with experience in distributed data processing using Azure Databricks and cloud-native data platforms. Familiar with workflow orchestration tools such as Apache Airflow, modern data engineering practices, and scalable pipeline design principles. Knowledgeable in APIs, microservices, DevOps CI/CD concepts, and Big Data ecosystem technologies including Spark and HDFS.
Fabtech Technical Campus - College Of Engineering And Research
B.TECH · AI & DS
December 4, 2022 – July 31, 2025
Enterprise Sales Analytics Pipeline on Azure
January 1, 2025 – June 1, 2026
Developed an end-to-end cloud-native sales analytics pipeline on Microsoft Azure, processing 50K+ sales records through Bronze, Silver, and Gold Lakehouse layers for scalable analytics workflows. Engineered distributed ETL/ELT pipelines using Azure Databricks and PySpark to cleanse, transform, standardize, and process large-scale structured sales datasets stored in ADLS Gen2. Designed and implemented analytics-ready Star Schema data models comprising fact and dimension tables to optimize reporting performance and enable efficient business intelligence querying. Implemented watermark-based incremental loading strategy to process only newly arrived records, reducing unnecessary data reprocessing and improving pipeline efficiency for batch workloads. Integrated Azure Synapse Serverless SQL with parquet-based Gold layer datasets to build reusable analytics views for revenue, customer, and product performance analysis. Built interactive Power BI dashboards featuring KPI cards, revenue trend analysis, product revenue insights, and top customer analytics.
Web Server Log Processing and Analytics Pipeline
January 1, 2025 – June 1, 2026
Engineered an end-to-end log processing and analytics pipeline to ingest, parse, and analyze semi-structured Apache web server logs using Python-based workflows. Developed scalable data cleaning and transformation processes using Pandas and Regular Expressions (Regex), converting raw log entries into structured analytical datasets. Implemented automated parsing logic for extracting timestamps, HTTP status codes, bandwidth metrics, filenames, and file extensions for traffic and error trend analysis. Designed modular analytics workflows to generate insights on hourly traffic distribution, 404 error patterns, bandwidth utilization, and HTTP response distribution. Integrated Microsoft Power BI dashboards with processed log datasets to visualize server performance metrics, traffic patterns, and operational KPIs. Optimized preprocessing and validation workflows by handling malformed records, missing byte values, and inconsistent request formats.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong alignment with the target role of Data Engineer, focusing on core data pipeline development, data modeling, and analytics. The breadth of technologies used in projects (Azure stack, PySpark, Python, SQL, Power BI) indicates a willingness to learn and adapt to various tools. However, the lack of professional experience and diverse project types beyond academic settings limits the assessment of broader cultural fit and adaptability to different organizational contexts.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to design end-to-end solutions and optimize data workflows, suggesting good problem-solving and operational thinking. The academic projects show initiative in applying learned concepts to practical scenarios. However, without psychometric test results or direct experience, it's difficult to fully assess stress handling, team collaboration, or work attitude.