Saivamshi Ausula
Data Engineer - Python, SQL • United States • s*************@gmail.com • +17******091 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Experienced Data Engineer with 4 years of experience designing and optimizing cloud native data platforms using Python, SQL, PySpark, AWS, Databricks, Snowflake, and Airflow. Built scalable ETL/ELT pipelines processing millions of records, improving pipeline efficiency by 40% and reducing data latency by 35%. Proven expertise in data warehousing, Lakehouse architectures, and real-time data processing, delivering reliable, high-quality data solutions that drive analytics and business decision making.
Technical Skills
Programming Languages: Python,Scala
Big Data & Distributed Processing: PySpark,Hadoop,Hive,HDFS,Databricks,Delta Lake
Cloud & Data Warehousing Platforms: AWS,S3,Glue,EMR,Lambda,Kinesis,RDS,Snowflake,Redshift,Teradata
Data Integration & Pipeline Tools: Apache Airflow,dbt,SSIS,Talend,Apache Kafka,ETL / ELT,Dimensional Modeling
Business Intelligence & Analytics: Tableau,Power BI,SSAS,KPI Reporting
DevOps & Infrastructure: Terraform,Docker,Jenkins,Git,CI/CD pipelines
Databases: SQL,PostgreSQL,SQL Server,MongoDB,T SQL
AI/ML & Data Processing Technologies : Pandas,NumPy,RAG,LangChain
Work Experience
Nationwide
Data Engineer
May 2025 – Present
Major US insurance and financial services firm; built cloud data platforms that supplied high-volume policy and claims data to analytics and BI teams.
- Engineered scalable ETL pipelines using Python, SQL processing large-scale insurance datasets in Amazon S3, improving data ingestion performance by 35%.
- Designed and optimized data pipelines using Apache Spark, PySpark, and Delta Lake on Databricks, enabling efficient handling of multi-terabyte datasets and improving data reliability for analytics teams.
- Resolved data latency and processing inefficiencies by implementing Apache Airflow-based workflow orchestration and real-time streaming with Kafka, reducing pipeline delays by 40%.
- Collaborated with cross-functional teams of 10+ engineers, analysts, and stakeholders, integrating data solutions into Power BI and Tableau dashboards, enhancing business reporting and decision-making capabilities.
- Implemented cloud-based data warehousing solutions using Snowflake and Amazon Redshift, along with CI/CD pipelines via Git, improving deployment efficiency by 30% and ensuring robust data validation and modeling practices.
LTIMindtree
Hyderabad, India
Data Engineer
Jul 2022 – Nov 2023
Global IT consulting and digital services provider; engineered cloud data pipelines for clients’ finance and operations analytics and BI modernization.
Tech Stack: Python, SQL, PySpark, Apache Spark, Snowflake, AWS, Apache Airflow, Git
- Engineered scalable ETL pipelines using Python and PySpark to process over 5M+ daily transactional records, reducing data latency by 35%.
- Designed and implemented dimensional data models in Snowflake to support enterprise BI reporting across finance and operations teams.
- Resolved data quality inconsistencies by implementing validation rules and automated reconciliation checks, improving accuracy by 28%.
- Led collaboration with cross-functional teams of 8 developers and analysts to migrate legacy SQL workflows to Spark-based distributed systems.
- Automated data orchestration using Apache Airflow DAGs, improving workflow reliability and reducing job failures by 30%.
SMG Info Tech Pvt Ltd
Hyderabad, India
Data Engineer
Jan 2021 – Jun 2022
Technology services company focused on business data solutions; created AWS-based ETL and warehousing to feed accurate management dashboards for multiple clients.
Tech Stack: Python, SQL, MySQL, PostgreSQL, MongoDB, AWS EC2, Git
- Built SQL-based ETL processes to transform raw business data into structured warehouse tables, improving reporting accuracy by 25%.
- Developed automated data extraction scripts using Python for multiple client systems, reducing manual reporting effort by 40%.
- Addressed performance bottlenecks in complex joins and aggregations by rewriting optimized SQL queries, reducing execution time by 30%.
- Implemented data validation and cleansing techniques to ensure high-quality reporting datasets for management dashboards.
- Supported migration of on-premise databases to AWS cloud infrastructure, enhancing system scalability and reliability.
Education
Clark University
Master of Science in Data Analytics • Worcester, MA • Jan 2024 – Dec 2025
Chaitanya Bharathi Institute of Technology
Bachelor of Technology in Electronics and Communications Engineering • Hyderabad , India • Jun 2018 – Jun 2022
Certifications
Certified AWS Data Engineer - Associate — Amazon Web Services
Earned AWS Solutions Architecture — Forage
Completed Tata GenAI Powered Data Analytics - Tata Consultancy Services — Forage
Accomplished Data Analytics Virtual Internship - Deloitte — Forage
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