Ramesh Mudavath

Experience: About 5 years of experience in Data Engineering, leading data pipeline architecture and optimization for tech leaders like Credit Suisse and Draxo Infotech driving significant improvements in data processing and analytics.
Technical Proficiency: Developed a 25% more efficient data processing system and a 20% faster data acquisition process at Credit Suisse, demonstrating hands-on expertise with AWS, Hadoop, Hive, and Spark for scalable, enterprise-level data solutions.

Cloud & Data Platform: Proficient in cloud services and big data tools, including AWS Redshift, Kafka, Kinesis, AWS EMR, AWS, Airflow, and Databricks, facilitating enhanced data workflows and infrastructure provisioning.
Programming : Adept in Python, Scala, and comprehensive SQL, with a proven ability to manage both batch and real-time data pipelines, ensuring seamless data exploration, preparation, and analytics. DevOps & Agile Methodologies: Skilled in integrating DevOps practices and Agile project management to streamline data operations, contributing to improved CI/CD processes, system performance, and proactive issue resolution.

Leadership & Collaboration: Recognized for mentoring junior data engineers and spearheading collaborative projects that align with business principles, coding standards improvements, and data governance initiatives.
Data-Driven Results & KPI Focus: Led analytical projects utilizing Tableau to distill complex data into actionable insights, significantly enhancing operational efficiency and guiding data- informed decision-making for large-scale tech environments.

Develop a data normalization and consolidation process using AWS Glue, reducing data redundancy by 40% and improving data quality scores by 50%, which directly increased the accuracy of machine learning models by 15%

Utilize PySpark to clean and enrich data by performing operations like data deduplication, handling missing values, and joining datasets.

Accomplish data importation from diverse sources, utilized Hive, Pig, and MapReduce for data transformation, and maintained data integrity within HDFS.

Responsible for developing a data pipeline with Amazon AWS to extract the data from weblogs and store it in HDFS and worked extensively with Sqoop for importing metadata from SQL.

Automate efficient data pipelines that parsed and stored raw data into partitioned Hive tables, improving data retrieval for reporting and analysis by 20%.

Establish ETL workflows using Apache Spark and Python, resulting in a 30% reduction in data processing time and improved data accuracy.

Installed and configured Apache Airflow for the S3 bucket and Snowflake data warehouse and created dags to run the Airflow.

Masters Degree in Data Science