PREETHI RAKALA
Summary
Results-driven Data Engineer with 5+ years of experience building cloud-native data platforms and high-performance data pipelines on AWS. Specialized in transforming raw, complex data into trusted, analytics-ready datasets through robust ETL frameworks, distributed processing, and automated data quality systems. Proven ability to partner with cross-functional teams to deliver business-critical insights, improve data reliability, and support advanced analytics and AI-driven use cases.
Overview
6
6
years of professional experience
Work History
Data Engineer
Mapfre
06.2024 - Current
- Established and maintained end-to-end data pipelines using AWS Glue, AWS Lambda, and Amazon EMR, increasing data processing efficiency by 35% and significantly reducing pipeline runtimes.
- Developed robust ETL and ELT pipelines to extract, transform, and load data from multiple sources into Amazon S3, Amazon Redshift, and Amazon Aurora, improving data ingestion time by 25%.
- Designed and scaled distributed data pipelines using Apache Spark on EMR and AWS Step Functions, reducing large-scale data processing time by 40%.
- Performed data cleaning, feature scaling, and feature engineering using Python (Pandas, NumPy, PySpark) to prepare high-quality datasets for analytics and machine learning use cases.
- Developed optimized SQL and Spark-based extraction logic to process large volumes of structured and unstructured data from APIs, streaming platforms, and cloud storage systems.
- Optimized BI and analytics dashboards by connecting Power BI / QuickSight to Amazon Redshift, Athena, and Databricks on AWS, reducing report load times by 20% and enabling near real-time business insights.
- Remote
Data Engineer
Tata Consultancy Services
04.2023 - 08.2023
- Maintained data pipeline up-time of 99.8% while ingesting streaming and transactional data across 8 different primary data sources using Spark, Redshift, S3, and Python.
- Automated ETL processes across billions of rows of data, which reduced manual workload by 29% monthly.
- Ingested data from disparate data sources using a combination of SQL, Google Analytics API, and Salesforce API using Python to create data views to be used in BI tools like Tableau.
- Communicated with project managers and analysts about data pipelines that drove efficiency KPIs up by 26%.
Programmer Analyst
Amazon
01.2020 - 03.2023
- Built and maintained AWS-based data pipelines to ingest operational and system logs from multiple platforms into centralized data lakes (Amazon S3, AWS Glue, Amazon Redshift) to support failure analysis and root-cause investigations.
- Implemented comprehensive data cleaning and validation frameworks using Python, PySpark, and SQL to handle missing values, anomalies, duplicates, and inconsistent schemas, significantly improving data reliability for reporting and analytics.
- Designed and developed data models and curated analytical datasets focused on identifying failure patterns, incident trends, and system performance gaps across large-scale operations.
- Supported leadership and operational teams by delivering deep-dive analyses, ad-hoc investigations, and executive reporting focused on continuous improvement and operational excellence.
Education
Masters in IT - Information Technology
Webster University
St louis04.2025
Bachelors of computer science - Computer Science
Sreyas Institute Of Engineering
Nagole, India05.2019
Skills
- Python
- ETLs
- SQL (Postgres, Redshift, MySQL)
- NoSQL (MongoDB)
- Spark, Kafka, Airflow
- AWS (Athena, Lambda, S3)
- Django
- Numpy, Pandas
- Databricks
- Snowflake, AWS
- Data Warehousing
- Data Pipeline Development, Machine Learning
- Apache Spark
- BigQuery
- Data Lakes
Timeline
Data Engineer
Mapfre
06.2024 - Current
Data Engineer
Tata Consultancy Services
04.2023 - 08.2023
Programmer Analyst
Amazon
01.2020 - 03.2023
Bachelors of computer science - Computer Science
Sreyas Institute Of Engineering
Masters in IT - Information Technology
Webster University