Santosh V Email & Phone Number

Leander, Tx, Us

Houston, Texas, United States

Leveraging a tech stack that includes Spark, YARN, Hadoop, Pig, HDFS, Hive, Scala, Python, Cloudera, MongoDB, Oozie, Airflow, Docker, Kubernetes, Drone, Kafka, MapReduce, Sqoop, ETL, and SQL, I spearheaded and managed end-to-end data engineering initiatives at Tailored Brands, a leader in [specific industry]. built and kept up-to-date reliable data pipelines for ingesting, processing, and transforming big datasets, assuring excellent data quality, dependability, and availability.In order to comprehend data requirements and create customized solutions that promote business insights and efficiency, I closely collaborated with cross-functional teams made up of data scientists, analysts, and software engineers.Adherence to industry norms and laws was ensured via the implementation of best practises for data governance, security, and compliance.

Fallon County, Montana, United States

spearheading end-to-end data engineering procedures from data intake to ETL processing to support the analytical and reporting requirements of multiple business divisions. worked collaboratively with cross-functional teams to comprehend data requirements, create data models, and put into place reliable data pipeline solutions. HDFS, Pig, Hive, and Spark were used as part of the Hadoop ecosystem to process and analyse big datasets quickly, allowing for data-driven decision-making. Used tools like Oozie and Airflow to design, implement, and maintain complex ETL workflows, assuring timely and accurate data processing.used Docker containers to install and scale data processing apps easily, improving productivity and maintainability. Kafka was used to implement real-time data streaming, enabling quick analyses and responses to altering data circumstances.

Hyderabad, Telangana, India

Using PySpark and Apache Spark, we designed and built scalable data pipelines that cut the time it takes to process data by 30%.created a containerized data processing environment using Cloudera and Kubernetes, which increased deployment speed by 40% and decreased resource usage.led the transition of ETL operations from outdated systems to cutting-edge Big Data technologies, which increased data processing capacity by 50%.Instant analysis of crucial business events is now possible thanks to the design and implementation of real-time data streaming pipelines using Kafka and Spark Streaming.worked with cross-functional teams to enhance data quality and accuracy on multiple reporting platforms by optimising data modelling methodologies.