Swetha K

About Swetha K

I am a Data Engineer with over 11 years of experience building large‑scale, cloud‑native data platforms across AWS and GCP. My work spans AI engineering, big‑data processing, and modern ETL/ELT development. I have hands‑on expertise designing and deploying AI agents using AWS Bedrock to support natural‑language queries, automated decision workflows, and intelligent data validation. I’ve built secure, scalable architectures using Lambda, API Gateway, Step Functions, Glue, EMR, Redshift, DynamoDB, and S3, and have deep experience with Hadoop, Spark, Hive, Kafka, and Airflow for processing high‑volume datasets. My background includes developing end‑to‑end pipelines in Python, PySpark, and Scala, enhancing them with AI‑driven anomaly detection and metadata generation. I’m skilled in data modeling, data quality, and warehouse design, and have delivered solutions using BigQuery, Dataflow, Pub/Sub, Dataproc, and Cloud Composer on GCP. I also bring strong experience in cloud security, IAM, Terraform, CloudFormation, CI/CD, and performance tuning, with a track record of migrating on‑prem systems to cloud‑native architectures and supporting production‑grade, enterprise‑level data environments.

Experience

1. Senior Data Engineer

   Wells Fargo

   Mar 2025 — Present · Charlotte, NC, US

   I have strong experience building AI‑driven data engineering solutions on AWS, with a major focus on Bedrock‑powered AI Agents and large‑scale data processing. I’ve designed and implemented custom AI Agents using AWS Bedrock to handle natural language queries, summarize complex datasets, and support automated decision-making for banking use cases. These agents were integrated with Lambda, API Gateway, and enterprise data sources to enable real‑time, intelligent data processing. I also enhanced ETL pipelines by using Bedrock models for AI‑driven validation, anomaly detection, and metadata generation, improving data quality and reducing manual effort.My background includes designing secure, scalable architectures for deploying AI solutions in regulated financial environments. Beyond AI, I’ve built large‑scale data processing systems using Hadoop, Spark, MapReduce, EMR, and designed Hive data models for enterprise analytics. I have hands‑on expertise with AWS services such as Glue, S3, Athena, EC2, RDS, and have developed automated ETL pipelines using Glue and Step Functions.I’ve integrated Airflow with S3, Redshift, and Snowflake for reliable data movement and built event‑driven ETL processes, including migrating data from DynamoDB to Redshift using Lambda and Python. I also have working knowledge of SAS Macros, Proc, and SQL for data manipulation and reporting.