Debashon Chakraborty

About Debashon Chakraborty

Results-oriented Data Engineer with 6 years of experience in designing, building, and optimizing scalable data pipelines and ETL processes for large-scale systems. Proficient in Apache Spark, Hadoop, Kafka, and Flink for real-time data streaming and analytics. Expertise in data modeling, cloud platforms (AWS, GCP, Azure), and databases (Snowflake, Redshift, Big Query) to enable efficient data storage and processing. Skilled in Python, Scala, SQL, and automation of workflows using Apache Airflow and Prefect. Adept at implementing data governance, security, and machine learning pipelines to drive AI-powered insights. Strong problem-solver with a track record of delivering high-performance, scalable, and secure data solutions in Agile environments.My hands-on experience with Big Data frameworks like Hadoop, Spark, and Kafka allows me to efficiently process data and enable real-time analytics. I am committed to ensuring data integrity and enhancing performance through meticulous design and optimization strategies. Known for collaborating closely with cross-functional teams, I deliver actionable insights that drive strategic business decisions and support organizational goals through data-driven approaches.Achievements- Developed scalable ETL pipelines and data warehouses using leading-edge technologies- Optimized data workflows with Python, Java, and SQL, ensuring efficient data processing- Engineered advanced analytics systems with Hadoop, Spark, and Kafka for real-time analytics- Enhanced data integrity and performance through meticulous design and optimization- Delivered actionable insights by collaborating with cross-functional teams for strategic decisions.

Experience

1. Data Engineer

   Ford Motor Company

   Jan 2024 — Present · Houston, TX, US

   In Ford Motors I am working as a Data Engineer, managing Data ETL Pipeline Designed and implemented scalable data pipelines using Apache NiFi, processing real-time vehicle telemetry data frommillions of connected vehicles. Built data lakes on AWS S3, enabling cost-effective storage and efficient retrieval of high-volume sensor data for analytics andmachine learning. Developed ETL workflows using Python and PySpark, transforming raw vehicle sensor data into structured datasets forpredictive maintenance and fleet optimization. Implemented real-time data streaming solutions with Apache Kafka and AWS Kinesis, ensuring seamless integration of IoTtelemetry into analytics platforms. Optimized Amazon Redshift database performance by fine-tuning SQL queries, reducing execution time and improvingreporting efficiency for engineering teams. Built and automated machine learning pipelines using ML flow, facilitating predictive analytics for vehicle diagnostics andanomaly detection.

Education

• American International University-Bangladesh

  BSc, Computer Science

  2013 — 2018

• Lamar University

  Master's degree, Management Information System

• Comilla Victoria govt college

  HSC, Science