Mridul Pant is an accomplished Data Engineer with over 6 years of experience in designing, developing, and optimizing ETL pipelines, data platforms, and real-time data workflows. He possesses a strong command of data engineering tools and frameworks, including Pyspark, Airflow, Kafka, Hive, Hadoop, and AWS cloud services (S3, Glue, EMR, ECS).His expertise spans multiple aspects of the data engineering lifecycle — from data ingestion and transformation to data warehousing and analytics. He has demonstrated his ability to build scalable data pipelines that process data from diverse sources (APIs, databases, SFTP, and streaming platforms) into modern data warehouses like Redshift, BigQuery, and Snowflake.He has a proven track record of automating workflows and enhancing system performance, notably optimizing BigQuery costs by 40%, reducing SLA times from 24 hours to real-time, and building event-driven microservices to enable seamless data flow. His work has improved system reliability, security, and operational efficiency.Mridul is also skilled in real-time data streaming using Kafka and Debezium to enable CDC (Change Data Capture) pipelines, ensuring low-latency data availability. He has experience in event-driven microservices development using Spring Boot, Docker, and AWS ECS, enabling seamless integration of services. With hands-on experience in data security, incident management, and system monitoring (using Loki, Grafana, and Tableau dashboards), he ensures continuous system availability and data integrity. His experience with reverse ETL, CDC, and self-serve ETL frameworks makes him a well-rounded engineer capable of simplifying complex data workflows for analytics and reporting. His technical toolkit includes Python, SQL, Java, Shell scripting, and DevOps tools like Git, Jenkins, and Docker. He has contributed to high-impact projects at companies like British Petroleum, Bukuwarung, Rupeek, and Enquero Global, earning recognition like the **Spot Award for extraordinary service.