Uday

PROFESSIONAL SUMMARY AWS Data Engineer with 5+ years of experience building scalable ETL pipelines and cloud data platforms using PySpark, AWS Glue, S3, Redshift, Athena, and Airflow. Experienced in data lake architecture, large-scale data processing, Spark optimization, incremental data loads, and CDC pipelines. Strong expertise in designing end-to-end data pipelines handling 10M+ records/day, improving ETL performance, and enabling data-driven analytics for business teams. CORE SKILLS Programming Languages: Python, Pandas and SQL. Big Data Technologies: Pyspark, Apache Spark. Databases: SQL Cloud Platforms: AWS: S3, Glue, Glue Crawlers, Glue Catalog, Athena, Redshift, Lambda, Cloudwatch and IAM. Orchestration Tools: Apache Airflow Data Engineering: ETL Pipeline Development, Data Lake Architecture, Data Quality Checks, Spark Performance Optimization, Partitioning and Broadcast Join. Storage Formats: Parquet, CSV, JSON. Version Control: Git IDE:Jupyter Notebook, PyCharm PROFESSIONAL EXPERIENCE 1. Data Engineer (Entry level) | YoulogixInfotechPvtLtd Nov 2020 – Mar 2022 •Assisted in setting up a cloud-based data lake that streamlined analytics and reporting across teams. •Developed data ingestion processes using AWS S3 and Glue, enabling faster integration of new data sources. •Created and maintained Athena queries to support self-service reporting and reduce dependency on engineering. •Gained strong hands-on experience in Spark and AWS cloud services while working on large-scale data processing. 2. Data Engineer(Junior level) | AT IndiaAutopartsPvtLtd Apr 2022 – Apr 2025 •Streamlined sales reporting workflows, reducing manual effort and accelerating report delivery cycles. •Partnered with business teams to design efficient Redshift models supporting financial and operational analytics. •Coordinated ETL scheduling and monitoring with Airflow to improve system reliability and minimize downtime. •Guided my peers on SQL tuning and AWS Glue best practices, enhancing overall team productivity. 3. Data Engineer (Mid level)| Featherlite Office Systems Apr 2025 – Jan 2026 •Leading the design and enhancement of customer transaction data pipelines to generate actionable insightsfor Amazon e – commerce analytics. The pipeline processes approximately 50–60 GB of raw data every night, supporting 14 dashboards and over 120 KPIs. •Collaborate with analysts and stakeholders to define KPIs and ensure data-driven decision-making. •Implement automated validation checks, reducing reporting errors and improving trust in data. •Enhance scalability by fine-tuning PySpark transformations, significantly reducing processing time. These optimizations reduced ETL runtime by about 25% and maintained 99.9% data accuracy across ~10 million records daily.