Professional Overview
Data Engineer with 4+ years of experience working on both AWS and Azure cloud platforms across two organizations. Skilled in Azure Data Factory, Databricks, ADLS, AWS Glue, EMR, Redshift, PySpark, and SQL. Experienced in building scalable ETL pipelines, lakehouse architectures, and optimizing high-volume data processing workflows.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built ETL pipelines using AWS Glue, EMR, S3, PySpark, and SQL
- Developed lakehouse architecture with Bronze, Silver, Gold layers
- Optimized Redshift queries and Spark jobs, reducing runtime by 35%
- Automated batch workflows using Airflow and AWS Glue Workflows
- Monitored pipelines with CloudWatch for performance and reliability
Key Achievements / KPIs
- Processed 200+ GB of data daily across enterprise ETL pipelines
- Reduced pipeline runtime by 35% through Spark and SQL optimization
- Improved query performance by 40% using Redshift tuning techniques
- Automated workflows, reducing manual effort and operational delays
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built ETL pipelines using AWS Glue, S3, and Apache Airflow
- Optimized Star/Snowflake schemas for scalable data retrieval
- Tuned SQL and PySpark jobs, improving performance by 50%
- Monitored pipelines for data quality, uptime, and reliability
- Delivered business-ready datasets using Amazon Redshift
Key Achievements / KPIs
- Processed 200+ GB of enterprise data daily through ETL pipelines
- Reduced ETL pipeline runtime by 35% using Spark optimization
- Improved SQL query performance by 50% through tuning techniques
- Automated workflows, reducing manual effort and operational delays
Education
Field of Study: Cloud computing
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
