Kenche Prashanth
Fort Wayne
Summary
4+ years of experience in the data domain, including data engineering, data cleaning, data wrangling, data transformation, and data analysis. Hands-on experience with the Hadoop ecosystem, including Spark, HDFS, MapReduce, Hive, Pig, Sqoop, Oozie, and Kafka. Proficient with AWS services such as EMR, S3, EC2, Glue, Athena, and Redshift. Experience transferring data from RDBMS systems to HDFS and Hive tables using Sqoop. Hands-on experience in exploratory data analysis using numerical functions and visualizations to support feature engineering. Strong experience developing PySpark scripts to manage data transformations and data delivery for batch and streaming workloads. Designed and developed streaming pipelines using Apache Kafka and PySpark from multiple sources such as APIs and data lakes to optimize monitoring performance. Developed PySpark jobs in Databricks to implement ETL pipelines sourcing data from S3 and loading into Snowflake. Experience creating Hive tables, partitions, and buckets and writing HiveQL queries to optimize performance. Developed data migration shell scripts in Linux to load data from DB2 into Hive tables while applying necessary transformations using big data technologies. Hands-on experience across big data lifecycle phases including data ingestion, data analytics, and data visualization. Expertise in SQL, including window functions, CTEs, complex joins, and advanced date, time, and conditional aggregations. Experience designing time-driven and data-driven automated workflows using Oozie. Created shell scripts to orchestrate pipeline architectures involving Python, SQL, Syncsort ETL, and ThoughtSpot jobs.
Overview
6
6
years of professional experience
Work History
Data Engineer
T-Mobile
Remote
05.2025 - 11.2025
- Gathered and analyzed system requirements to design scalable data warehousing solutions using Snowflake and AWS.
- Utilized AWS services such as Redshift, S3, and Glue to design a scalable data warehouse, improving query performance by 35%.
- Implemented data ingestion frameworks using Apache Kafka, enabling real-time processing and reducing data latency by 40%.
- Designed and maintained data models to support seamless integration with T-Mobile analytics and reporting platforms.
- Created BigQuery authorized views to enforce row-level security and securely expose data to cross-functional teams.
- Worked with Google Data Catalog and other Google Cloud APIs to monitor, analyze, and optimize BigQuery usage, queries, and billing.
- Used Cloud Shell and SDK in GCP to configure Data Proc, Cloud Storage, and BigQuery and to load and transform large structured, semi-structured, and unstructured datasets (TXT, ZIP, XML, JSON).
- Designed and implemented data ingestion techniques for data coming from various source systems into cloud data platforms.
- Designed and developed Spark applications using Python, PySpark, and Spark SQL for high-volume data processing and managed end-to-end production deployments.
- Maintained fully automated CI/CD pipelines for code deployment using GitLab and Jenkins.
- Automated data validation and cleansing workflows, improving data quality and accuracy by 25%.
- Led the migration of on-premises data processes to cloud-based architectures, improving scalability and reliability of T-Mobile’s data infrastructure.
- Monitored and orchestrated data workflows using Apache Airflow to ensure timely data availability for reporting and analytics.
- Collaborated with business stakeholders to translate requirements into scalable technical data solutions, enabling data-driven decision-making.
- Tuned complex SQL queries and data models, achieving a 20% reduction in query execution time.
- Mentored junior engineers on ETL best practices, data modeling, and cloud data architecture.
- Technical Environment: AWS (Redshift, S3, Glue), Apache Kafka, SQL, Apache Airflow, Python, PostgreSQL, Git, Jenkins, Linux, Hortonworks 2.5, HDFS 2.7.3, SPARK 2.0.0, Hive 2.0.0, YARN
Data Engineer/Analyst
EC-Council
Hyderabad
05.2022 - 07.2023
- Analyzed financial data to identify trends in customer behavior, loan performance, and credit risk, providing insights to support strategic decisions.
- Developed ETL pipelines to integrate data from multiple banking systems, ensuring accurate and timely data availability for analysis and reporting.
- Wrote SQL queries to extract, clean, and transform data for key metrics such as loan delinquency rates and portfolio performance.
- Built interactive Power BI dashboards to visualize customer segmentation, transaction patterns, and operational efficiency for stakeholders.
- Performed data validation and quality checks to maintain high accuracy and consistency in financial reporting.
- Collaborated with risk management teams to model and forecast credit risk using Python-based statistical analysis, improving risk assessment.
- Optimized database performance for large datasets, reducing report generation time by 30%.
- Designed and implemented data-driven monitoring solutions to detect and prevent fraud, contributing to a 20% reduction in fraud incidents.
- Automated reporting workflows using SSIS, reducing manual processing effort by 40% and improving operational efficiency.
- Partnered with compliance and audit teams to ensure that data handling, storage, and reporting adhered to regulatory and governance standards.
- Technical Environment: SQL Server, Python, Power BI, SSIS, Apache Spark, PostgreSQL, AWS, and Excel for data analysis, ETL, reporting, and data visualization.
Data Engineer/Analyst
Lasya Infotech
Hyderabad
07.2019 - 02.2022
- Developed and implemented data pipelines using PySpark, AWS Lambda, and APIs to process over 100 million records and store curated data in Hive.
- Built PySpark jobs in Databricks to collect, clean, and transform data from S3 into Hive tables.
- Developed AWS Lambda functions to trigger jobs that process data and store outputs in multiple S3 buckets.
- Used Hive to analyze partitioned and bucketed datasets and compute metrics for reporting.
- Created Sqoop scripts to import, export, and update data between HDFS and PostgreSQL.
- Automated ETL workflows using Oozie to orchestrate Python, Hive, and PySpark jobs running on AWS EC2.
- Developed Hive scripts to parse raw data, populate staging tables, and store refined, partitioned data in S3.
- Wrote Python scripts to perform sensitive data masking on ServiceNow extracts, applying different rules for different views.
- Converted and optimized legacy SQL scripts into PySpark SQL for improved performance and scalability.
- Implemented Kafka consumers to ingest data from topics every 15 minutes and land data into S3.
- Created Hive databases, tables, and views with appropriate access controls to support multiple user groups.
- Performed rigorous data quality checks to validate that extracted data matched destination view schemas and business rules.
- Developed Tableau dashboards to help end users understand incident and change-ticket trends for technology organizations.
- Built Tableau reports to track daily incident trends and resolution rates using snapshot data sourced from Snowflake.
- Technical Environment: Pyspark, Hive, SQL, Kafka, PysparkSQL, Sqoop, Oozie, shell scripting, Linux, Python, Tableau, AWS EC2, S3, Lambda, ServiceNow
Education
Masters - Information Systems
Indiana Institute of Technology
US05.2025
Skills
- Python
- SQL
- Java
- Scala
- ETL
- Data Warehousing
- Data Modelling
- Data Integration
- Apache Spark
- Hadoop
- Kafka
- SQL Server
- PostgreSQL
- Oracle
- MySQL
- Snowflake
- MongoDB
- AWS
- Azure
- Google Cloud
- Power BI
- Tableau
- Airflow
- Jenkins
- Git
- Docker
- Kubernetes
- Analytical Thinking
- Communication
- Collaboration
- Project Management
Timeline
Data Engineer
T-Mobile
05.2025 - 11.2025
Data Engineer/Analyst
EC-Council
05.2022 - 07.2023
Data Engineer/Analyst
Lasya Infotech
07.2019 - 02.2022
Masters - Information Systems
Indiana Institute of Technology