Post Job Free
Sign in

Data Engineer

Location:
Los Angeles, CA, 90007
Salary:
80000
Posted:
October 16, 2025

Contact this candidate

Resume:

Sesha Vaishnavi Srungaram

Data Engineer

******************@*****.*** +1-562-***-**** LinkedIn PROFESSIONAL SUMMARY

Visionary data architect and advanced Data Engineer with over 4 years of experience designing and optimizing robust Big Data and Cloud solutions on AWS (Glue, EMR, Kinesis) and Snowflake/Databricks. Expert in architecting scalable CI/CD, IaC (Terraform), and real-time data ingestion pipelines, spearheading large-scale migrations, and reducing operational costs by up to 60%. Successfully leveraged Spark/PySpark, dbt, Kafka, and Dimensional Modeling to enhance data quality and processing performance for high-volume financial analytics. TECHNICAL SKILLS

Cloud Data Platforms: AWS (Glue, EMR, S3, Kinesis, Lambda, Athena, Redshift), Microsoft Azure, Databricks, Terraform (IaC) Data Warehousing & Modeling: Snowflake, Teradata, Redshift, dbt (Data Build Tool), Dimensional Modeling (Star/Snowflake), Data Vault Big Data Processing: Apache Spark (PySpark/Scala), Apache Kafka (Streaming), Hadoop, Hive, MapReduce, Apache Flink Programming & Querying: Python (NumPy, Pandas), Advanced SQL, Scala, Java, Shell Scripting Databases: PostgreSQL, MySQL, Oracle SQL, MongoDB, HBase, Cassandra, DynamoDB DevOps & MLOps: CI/CD (Jenkins, GitHub), Docker, Kubernetes, MLOps Fundamentals Data Quality & Reporting: Data Governance, Data Quality, Power BI, Tableau, Matplotlib Certifications: Google Data Analytics Professional (Link) PROFESSIONAL EXPERIENCE

JP Morgan Chase & Co. FinTech USA Jun 2024 – Present Data Engineer

• Spearheaded the critical migration of 500+ legacy mappings from PowerCenter to a centralized, serverless AWS Glue environment using PySpark, decreasing batch job execution times by 40%.

• Automated EMR cluster lifecycle management (provisioning, scaling, termination) using AWS Lambda, CloudWatch, and Step Functions, achieving a measurable 60% reduction in operational overhead and cost.

• Architected a near-instant data ingestion layer by deploying Snowpipe routines for structured/semi-structured data streaming from S3 into Snowflake, enabling 10+ concurrent reporting users to achieve sub-second query response times.

• Implemented a real-time data processing pipeline utilizing AWS Kinesis and Spark Streaming to ingest and transform high-volume financial transactions, reducing critical reporting latency from minutes to mere seconds.

• Established end-to-end CI/CD pipelines via GitHub and Jenkins to automate the testing, packaging, and reliable deployment of 20+ Spark jobs on AWS EMR/Glue, improving deployment velocity by 5x.

• Collaborated on S3 data lifecycle policies and object versioning across the data lake, resulting in a 10% reduction in cloud storage costs while rigorously adhering to strict data governance policies. TCS Information Technology India Aug 2021 – Jan 2023 Data Engineer

• Optimized high-volume ELT pipelines on Databricks using Apache Spark, improving processing efficiency by 40% while successfully integrating and processing over 10 TB of daily data from 50+ disparate sources.

• Developed and integrated dbt (data build tool) tests directly into CI/CD pipelines, automating data validation and quality assurance across all deployments and reducing data quality incidents by 25%.

• Designed and improved 25+ dimensional data models (Star and Snowflake Schemas) within Teradata to support high-performance data marts for Sales, Operations, and Finance analysis.

• Produced Infrastructure as Code (IaC) using Terraform modules for defining and provisioning networking and compute resources, boosting environment deployment efficiency by 50%.

• Authored and fine-tuned complex SQL scripts for advanced data extraction, transformation, and analytical reporting across MySQL, PostgreSQL, and SQL Server, handling datasets up to 500 GB.

• Developed 10+ interactive Power BI dashboards for executive finance and operations teams, eliminating over 15 hours per week of manual data aggregation and reporting efforts.

Oval SoftTech Information Technology India Jun 2020 – Apr 2021 Jr. Data Engineer

• Facilitated the buildout of a scalable enterprise Data Lake foundation using the Hadoop ecosystem (HDFS, Hive), improving enterprise-wide accessibility to structured and unstructured data assets by 70%.

• Deployed a low-latency data streaming Proof-of-Concept (POC) using Apache Kafka and Spark Streaming with an HBase sink, processing millions of events per second and cutting ingestion latency by 50%.

• Created 15+ complex Apache Airflow DAGs to orchestrate and monitor multi-stage data processing tasks, providing centralized workflow management and increasing successful job completion rates by 15%.

• Executed a targeted database migration from relational systems to MongoDB, optimizing data storage architecture for semi-structured data and reducing average query complexity for relevant reports by 40%.

• Crafted and modernised transformation pipelines using HiveQL to minimize manual data preparation tasks by 4 hours per week, significantly increasing data freshness for consumption.

EDUCATION

Master of Science in Computer Science California State University, Long Beach, USA Bachelor of Technology in Computer Science GMR Institute of Technology, India



Contact this candidate