Sravya Keesara
Summary
Results-driven AWS Data Engineer specializing in scalable ETL pipeline design and data model optimization. Leveraged AWS services and Apache Spark to enhance query performance significantly. Led code reviews and knowledge-sharing sessions to elevate team productivity, while implementing data governance and self-service analytics to generate impactful business insights.
Overview
10
10
years of professional experience
1
1
Certification
Work History
AWS Data Engineer
Baxter
Chicago, USA
03.2024 - Current
- Developed real-time data ingestion pipelines with Kafka, AWS Lambda, and Kinesis, reducing data latency from hours to minutes across safety and compliance analytics.
- Designed and maintained end-to-end ETL/ELT pipelines using Databricks, Airflow, and DBT, delivering production-ready datasets for analytics and ML workloads hosted on AWS Cloud.
- Built and optimized data lake and warehouse models using Amazon S3, Redshift, and Glue, improving query performance by 35% and lowering storage costs by 20%.
- Integrated IoT sensor data from MES and manufacturing systems into AWS Lakehouse, facilitating predictive analytics and proactive equipment maintenance.
- Collaborated with DevOps and architecture teams to optimize Databricks cluster performance on AWS EMR, reducing runtime variance by 30%.
- Orchestrated infrastructure-as-code deployments via Terraform and CloudFormation, ensuring consistent and auditable AWS resource provisioning.
- Implemented CI/CD pipelines using Jenkins, GitLab, and Terraform, enabling automated build, test, and deployment of Databricks notebooks, DBT models, and AWS Glue jobs.
- Developed and optimized incremental and CDC-based ingestion pipelines using Kafka, DBT snapshots, and Snowflake-style stream patterns on AWS, reducing full reload dependency and improving efficiency.
- Led code reviews and knowledge-sharing sessions, establishing AWS-native best practices that improved pipeline reliability and developer productivity.
- Designed and managed metadata, lineage, and cataloging using AWS Glue Data Catalog, S3, and CloudWatch, improving data discoverability and governance across the AWS Lakehouse.
- Built monitoring and alerting frameworks using CloudWatch, SNS, and Lambda, ensuring early detection of pipeline failures and SLA breaches in Databricks and Glue environments.
- Collaborated with data science teams to operationalize ML feature pipelines using PySpark, Databricks, S3, and SageMaker, supporting scalable model training and inference workflows on AWS.
- Environment: AWS Cloud (S3, EMR, Glue, Lambda, Kinesis, Redshift, Step Functions, CloudWatch, SNS, IAM, SageMaker), Databricks, Apache Spark / PySpark, Apache Kafka, Airflow, DBT, Terraform, CloudFormation, Jenkins, GitLab, AWS Deequ, Lakehouse Architecture, CI/CD, Data Governance & Observability.
Data Engineer/ Software Engineer
JP Morgan Chase
Chicago, USA
05.2023 - 02.2024
- Engineered and deployed end-to-end ETL pipelines on AWS EMR and Glue using PySpark, integrating large-scale structured and semi-structured financial data across multiple source systems.
- Designed and maintained real-time streaming pipelines using Kafka, Redshift, and DynamoDB to deliver low-latency access to critical financial transaction data.
- Automated infrastructure provisioning and CI/CD workflows using Terraform, Docker, and Jenkins, reducing manual deployment time by 70%.
- Implemented data quality and validation frameworks in Python and Glue to ensure high data accuracy across production pipelines, enhancing data reliability.
- Partnered with business and analytics teams to translate functional requirements into data models and transformation logic, cutting onboarding time for new datasets from 3 weeks to 3 days.
- Optimized Spark jobs for parallel processing and partitioning, improving pipeline performance by 40%, and reducing EMR costs.
- Built monitoring and alerting dashboards in CloudWatch and Grafana to enable proactive detection of pipeline failures and latency bottlenecks, improving operational oversight.
- Designed and implemented incremental and CDC-based ingestion pipelines using PySpark, AWS Glue, and Kafka, minimizing full data reloads and improving pipeline efficiency.
- Implemented end-to-end data security and access controls using AWS IAM, KMS, and VPC configurations, ensuring compliance with financial data governance standards.
- Developed automated reconciliation and anomaly detection processes using Python, CloudWatch, and custom validation frameworks, improving trust in production datasets.
- Orchestrated complex data workflows using AWS Step Functions and Glue triggers, enabling reliable scheduling and dependency management across batch pipelines.
- Collaborated with DevOps teams to containerize Spark utilities using Docker and standardize deployment across EMR environments, improving portability and operational consistency.
- Environment: AWS (EMR, Glue, Redshift, DynamoDB, S3, Lambda, Step Functions, CloudWatch, KMS, IAM, VPC), Apache Spark / PySpark, Apache Kafka, Docker, Terraform, Jenkins, Grafana, Python, SQL, CI/CD, Agile/Scrum.
Data Engineer
Early Warning
Scottsdale, USA
06.2021 - 04.2023
- Designed and maintained scalable ETL workflows using Informatica PowerCenter and AWS Glue, supporting high-volume data migrations across enterprise banking systems.
- Integrated Kafka-based event streaming pipelines into Snowflake and Redshift, enabling near real-time updates for fraud detection and transaction monitoring systems.
- Optimized complex SQL queries, stored procedures, and transformation logic, reducing ETL processing time by 35%, and improving data throughput.
- Automated data ingestion from multiple financial systems to S3, Glue Catalog, and Redshift, enhancing schema validation and lineage tracking.
- Implemented data quality checks and reconciliation processes in Python, minimizing discrepancies across production and staging environments.
- Collaborated with QA, DevOps, and business stakeholders to ensure zero-defect releases, increasing system reliability and user confidence.
- Designed and implemented incremental and CDC-based ingestion pipelines using Kafka, AWS Glue, and Python, reducing full refresh cycles and improving processing efficiency.
- Built and optimized analytics-ready data models in Snowflake and Amazon Redshift, leveraging partitioning, clustering, and compression for high-performance reporting.
- Implemented end-to-end data governance and security controls with AWS IAM, encryption, and role-based access, ensuring adherence to banking regulatory standards.
- Developed automated reconciliation and audit validation frameworks in Python and SQL, improving data accuracy across upstream and downstream financial systems.
- Created monitoring and alerting mechanisms using CloudWatch and custom logging frameworks, facilitating proactive detection of ETL failures and SLA breaches.
- Partnered with architecture teams to modernize legacy Informatica workflows, transitioning critical pipelines toward cloud-native Glue-based ETL solutions.
- Environment: Informatica PowerCenter, AWS Glue, Apache Kafka, Snowflake, Amazon Redshift, Amazon S3, AWS Glue Data Catalog, CloudWatch, AWS IAM, Python, SQL, ETL/ELT, CDC Pipelines, Data Governance & Lineage, Banking Data Systems.
Big Data Engineer
AbbVie
Chicago, USA
08.2018 - 05.2021
- Migrated legacy on-prem databases into AWS Redshift and S3, establishing a cloud-first analytics platform for global data integration.
- Designed and developed Spark SQL pipelines in Python for cleaning, wrangling, and transforming structured and unstructured datasets, improving ML model accuracy by 20%.
- Tuned Spark workloads through dynamic partitioning, broadcast joins, and caching strategies, improving overall pipeline efficiency by 25%.
- Automated data ingestion and transformation workflows using AWS Glue and EMR, enhancing scalability and reducing manual data processing effort.
- Collaborated with business analysts to develop Tableau dashboards connected to Redshift, delivering near real-time insights into product performance and clinical metrics that informed strategic decisions.
- Implemented data quality checks and validation scripts to ensure consistency across multiple data layers in S3 and Redshift.
- Designed and implemented incremental and CDC-based ingestion pipelines using AWS Glue, PySpark, and S3, reducing full data reloads and improving pipeline reliability.
- Built and optimized analytics-ready dimensional and fact models in Amazon Redshift, leveraging distribution styles, sort keys, and compression for high-performance querying.
- Developed automated reconciliation and anomaly detection frameworks using Python and SQL, improving data accuracy across upstream and downstream data layers.
- Implemented end-to-end data security and access controls using AWS IAM, KMS, and bucket policies, ensuring compliance with enterprise data governance standards.
- Orchestrated complex data workflows using AWS Glue triggers and EMR scheduling, enabling reliable dependency management across batch processing pipelines.
- Partnered with data science teams to operationalize feature engineering pipelines using Spark SQL, PySpark, and Redshift, supporting scalable ML training and inference.
- Environment: AWS (Redshift, S3, Glue, EMR, IAM, KMS, CloudWatch), Apache Spark (Spark SQL, PySpark), Python, SQL, Tableau, Cloud Data Lake & Warehouse Architecture, Data Quality & Governance, CDC Pipelines, ML Feature Engineering.
Data Analyst
Tenpath Solutions
, India
01.2016 - 07.2018
- Ingested legacy SQL Server + Teradata data into Snowflake via S3 staging, enabling modernized analytics.
- Developed Spark SQL and Scala jobs to process raw data into structured, analysis-ready datasets.
- Conducted data profiling and validation to improve accuracy and consistency across multiple source systems.
- Partnered with business stakeholders to translate requirements into actionable KPIs and dashboards, driving data-driven decision-making.
- Automated recurring reporting workflows using Python and SQL, cutting manual effort by 40%, streamlining reporting processes.
- Developed Tableau dashboards and Excel performance reports, cutting manual reporting time by 50%, enhancing reporting efficiency.
- Performed ad-hoc analysis on large datasets to identify trends and anomalies, influencing strategic initiatives.
- Optimized SQL queries and ETL processes, improving performance and reducing query runtime by 30%.
- Designed interactive dashboards in Tableau/Power BI with drill-down capabilities, empowering self-service analytics for non-technical users.
- Designed and implemented analytics-ready semantic layers on Snowflake and S3, enhancing KPI consistency and accelerating dashboard development.
- Built and maintained automated data refresh and validation pipelines using Python, SQL, and Spark, ensuring reliable and timely delivery of BI datasets.
- Collaborated with data engineering teams to optimize data models and source extracts for BI consumption, improving dashboard performance and reducing query latency.
- Environment: Snowflake, Amazon S3, AWS Glacier, Apache Spark (Spark SQL, Scala), Python, SQL, Tableau, Power BI, Microsoft Excel, SQL Server, Teradata, BI & Analytics Platforms, Data Governance & Lineage.
Education
Bachelors - Electronics and Communications Engineering
JNTU Hyderabad
05-2017
Skills
- AWS services (EMR, S3, Redshift, RDS, DynamoDB, Lambda, Glue, Athena, Kinesis, Step Functions, CloudFormation, CloudWatch, Lake Formation, SNS, SQS, KMS, IAM, VPC)
- Apache Spark and PySpark
- Spark SQL and Streaming
- Databricks and Hadoop
- Hive and Kafka
- Kafka Streams and Airflow
- Python and Scala
- Batch and real-time pipelines
- Event-driven architectures
- Snowflake and Amazon Redshift
- Informatica PowerCenter and DBT
- Shell scripting and Jinja
- Git (GitLab CI/CD)
- Terraform and Jenkins
- Feature engineering pipelines
- PostgreSQL and MySQL
- SQL Server and MongoDB
- Cassandra and HDFS
- Data governance (HIPAA & GDPR)
- Role-based access control
- Tableau and Power BI
- KPI and self-service analytics
Certification
AWS Certified Solutions Architect – Associate, https://www.credly.com/badges/d262f39f-b1b4-417e-bcd7-5589487194cc/
Timeline
AWS Data Engineer
Baxter
03.2024 - Current
Data Engineer/ Software Engineer
JP Morgan Chase
05.2023 - 02.2024
Data Engineer
Early Warning
06.2021 - 04.2023
Big Data Engineer
AbbVie
08.2018 - 05.2021
Data Analyst
Tenpath Solutions
01.2016 - 07.2018
Bachelors - Electronics and Communications Engineering
JNTU Hyderabad