Tejesh Bathula Email & Phone Number

United States

Texas, United States

• Developed and maintained various DBT models and macros, focusing on incremental model design and modularization, resulting in a 40% increase in data pipeline efficiency and a 25% reduction in pipeline execution time.
• Applied AWS Lambda functions triggered by incoming files in S3 to perform data validation and transformation before loading into Amazon Redshift, enhancing data quality and pipeline efficiency.
• Used Amazon Kinesis Firehose to stream real-time data into data lakes for near real-time analytics, enabling faster insights.
• Developed and deployed Spark jobs using Python and PySpark, achieving a 10% reduction in processing time compared to traditional MapReduce pipelines.
• Created complex SQL queries to extract and transform data from relational databases, increasing the availability of data for analytics

India

• Accomplished 5 UDFs in Snowflake for complex data cleansing and normalization tasks, reducing data movement by 70% and achieving a 30% improvement in data loading times for downstream analytics.
• Designed and implemented data pipelines using technology (Apache Airflow, AWS Glue) to ingest data from various sources (relational databases, APIs) into target systems (data warehouse).
• Orchestrated a complex data processing workflow consisting of multiple stages (data extraction, transformation, loading) using AWS Pipeline, improving data pipeline reliability and 40% reduction in data processing.
• Established and deployed a high-performing ETL pipeline in Databricks to process terabytes of data daily, enabling efficient data integration and transformation for the data warehouse.
• Designed and implemented efficient HiveQL queries to analyze large datasets stored in HDFS, achieving a 30% reduction in query execution time compared to traditional methods.
• Configured Kafka topics and partitions, achieving a 20% increase in message throughput to meet the data ingestion needs of downstream application

India

• Used HiveQL queries joining data from an average of 5 sources, enabling analysts to complete complex reports in 20% less time.
• Utilized PySpark to clean and enrich data by performing operations like data deduplication, and handling missing values.
• Accomplished interactive Power BI reports leveraging advanced DAX and Power Query modeling techniques to unlock deeper data insights and facilitate informed decision-making.
• Implemented data pipelines using Apache Airflow on Dataproc to automate data processing tasks.
• Increased data accessibility by 30% by migrating legacy data warehouses to BigQuery's serverless architecture

Masters, Data Science, 3.91/4

• Excelled in predictive modeling, machine learning, and statistical analysis. • Led data science projects, including a comprehensive.

Postgraduate Degree, Data Science & Engineering

Activities and Societies: Great Lakes is one of the premier business schools in India known for its technology adoption & Analytics.

Bachelor Of Technology - Btech, Mechanical Engineering