Seeking challenging role in Big Data field for the purpose of designing scalable solutions. Having 2+ years of experience as Data Engineer working with distributed technology tools for developing Batch and Streaming pipelines using ETL tools. Working knowledge of Data warehousing, Data modelling, Governance, Data Ingestion, Data Processing and Data Architecture. Knowledge of database technologies such as SQL, NoSQL, and distributed databases. Designing Hadoop data analytics solutions using technologies and tools like Hadoop, Spark, Map Reduce ,HBase, HIVE, SCALA, Python, SQL, Airflow. Good experience in data validation, data wrangling, data transformation and data management. Experienced in CI CD pipeline and workflow management. Attained 25% growth in revenue by collaborating with senior data engineers and stakeholders in requirement gathering and analysis to build efficient and optimized data pipelines. Data Engineer Project 1 Served closely with 3 teams across the company to identify and solve business challenges utilizing large structured and unstructured data in a distributed processing environment. Ingested the daily volume of 3 GB property data using SQOOP. Creation of Sqoop jobs resulted in 15% reduce in latency in ingesting the data into the system. Maintained data pipeline up-time of 99.8% while ingesting transactional data. Optimized the existing pipelines using Spark to deal with the growing data requirements which resulted in reducing resources by 25% and fasten up the processing by 10x. Assisted in debugging the errors while processing data in Spark which reduced the runtime by 10%. Project 2 Aggregated structured data from 10+ sources to build the foundation of new data analytics platform; led to $10,000 in revenue. Automated ETL processes across millions of rows of data which helped in avoiding the manual workload by 20% monthly. Worked with a team of 5 data engineers in managing pipelines and perform data analysis using Hive to build dashboards saving 10 hours per week for manual reporting work.