Sadique T. is an accomplished Data Engineer with over three years of experience designing and optimizing scalable ETL and ELT pipelines and building cloud-based data solutions using Azure, GCP, AWS, and Snowflake. He demonstrates strong proficiency in Python, SQL, PySpark, data modeling including the Kimball methodology, ETL/ELT pipelines, Apache Airflow for orchestration, Azure Data Factory, and real-time data processing with Apache Kafka. Sadique is skilled in working with data warehousing, data lakes, PostgreSQL, ClickHouse, NoSQL, and operationalizes analytics readiness using tools such as Azure Synapse, Azure Data Lake, Databricks, BigQuery, Cloud Functions, Cloud Storage, Snowpipe, Lambda, Redshift, EC2, and Snowflake. His expertise extends to DevOps with Git, GitHub Actions, Docker, and Terraform, as well as analytics and visualization using Looker Studio, Grafana, Matplotlib, and Seaborn. He has a proven track record of migrating large datasets to cloud environments, optimizing SQL queries, developing end-to-end pipelines for real-time and batch data processing, and implementing advanced analytics dashboards. His portfolio includes key projects like a Netflix recommendation system with Flask integration, a Solana blockchain analytics platform, and comprehensive sales and e-commerce data pipelines leveraging technologies such as Airflow, Kafka, and PostgreSQL, consistently delivering improvements in data reliability, performance, and decision-making speed.