Sravya K Email & Phone Number

United States

United Staes

As a Data Engineer at Bosch, I developed and optimized scalable data pipelines on Google Cloud Platform (GCP) for processing and analyzing large datasets, focusing on ETL/ELT workflows. My responsibilities included implementing efficient ETL workflows to extract, transform, and load data into BigQuery, optimizing data models and tuning queries to enhance performance and reduce costs. I used Cloud Dataflow for real-time data processing, Pub/Sub for event-driven architectures, and automated data validation to ensure data quality and compliance.I designed and enforced data governance policies and IAM configurations for robust access control. Data storage in Google Cloud Storage (GCS) was optimized using Apache Parquet and ORC formats, improving performance and cost-efficiency. I also built interactive dashboards with Looker and Tableau to provide data insights and enable real-time monitoring.Automated data workflows using Apache Airflow and Python DAGs ensured efficient data integration and pipeline reliability. I utilized Terraform for Infrastructure as Code (IaC), streamlining deployments and maintaining consistent cloud environments. Collaborating with data scientists, I deployed AI/ML models in the cloud using TensorFlow/Keras for predictive analytics.I developed unit tests with PyTest and JUnit to validate code quality and utilized Jenkins pipelines for CI/CD. Additionally, I provided mentorship to junior engineers, conducted code reviews, and documented data pipelines and workflows to support team knowledge sharing. I troubleshooted cloud infrastructure issues, optimized resource usage, and collaborated in an Agile environment using Git for version control and Jira for project management. This experience highlights my expertise in data engineering, cloud solutions, and real-time data processing.

United States

As a Data Engineer at Elavon, I led a successful cloud data migration from on-premises databases to Google Cloud Platform (GCP), designing strategies to minimize downtime and ensure data integrity throughout the process. I built and optimized data models in BigQuery, leveraging storage formats like Parquet and ORC to enhance performance for analytics.Using Apache Spark, I managed large-scale data transformations, implementing automated quality checks with Python scripts to validate accuracy. I also configured real-time data ingestion pipelines with Apache Kafka and Google Pub/Sub, ensuring scalability and resilience in data flow. Data integration workflows were automated through Apache Airflow, which I utilized to orchestrate and streamline ETL processes across multiple cloud platforms.To maintain compliance, I enforced data governance policies and implemented data quality monitoring with validation scripts. I also developed IAM policies and encryption measures for data security during migration. Infrastructure automation was achieved using Terraform, which ensured consistent deployments across GCP, AWS, and Azure.I trained team members on best practices in GCP and data migration, providing knowledge transfer and supporting skill development. Additionally, I created interactive dashboards in Tableau and Looker to monitor migration progress and validate data accuracy. Performance tuning of Apache Spark jobs was a key focus, enabling optimized data processing speed and efficient resource utilization.For testing and stability, I developed unit tests using PyTest and JUnit, ensuring robust pipeline functionality. I documented migration processes, data models, governance policies, and automation scripts comprehensively, establishing a valuable resource for future reference. This project showcases my skills in cloud data migration, real-time data processing, and automation within a secure, compliant environment.

As a Data Engineer, I designed and implemented a real-time data processing solution utilizing Apache Kafka for event streaming and integrated Apache Flink for advanced stream processing and analytics. I developed data ingestion pipelines using Python to collect, process, and enrich streaming data from multiple sources, significantly enhancing data accuracy and relevance.I built interactive dashboards with Tableau to monitor data streams in real-time, supporting data-driven business intelligence and operational decision-making. For data storage, I used cloud solutions like Amazon S3 and Azure SQL Database, ensuring scalability, data integrity, and effective management of processed data. Additionally, I deployed AI/ML models in collaboration with data scientists, using TensorFlow/Keras for real-time predictions on streaming data to enhance model performance.To maintain resilience and fault tolerance, I automated the deployment and scaling of data processing applications using Docker and Kubernetes. This setup allowed for robust data workflows that I managed and optimized through Apache Airflow, creating a streamlined deployment of data processing tasks across Docker and Kubernetes. I tuned Kafka clusters to reduce latency and maximize throughput, optimizing the performance of real-time data processing.For quality assurance, I developed test cases using JUnit and PyTest to validate data processing logic and ensure pipeline stability. I also managed source code using Git, collaborated in an Agile environment, and leveraged Jenkins CI/CD pipelines for continuous integration and delivery, enhancing productivity and deployment reliability. This experience highlights my expertise in real-time data engineering, cloud environments, and end-to-end automation for data-driven applications.

Developed a data lake architecture on AWS to consolidate data from various sources, enabling centralized data storage and streamlined access for analytics teams.Built scalable ETL pipelines using Python and Apache Spark to ingest, process, and transform data from multiple on-premise and cloud data sources.Utilized Amazon Redshift and S3 for data warehousing and storage, optimizing data retrieval times and reducing costs through automated lifecycle management policies.Established a Kafka-based streaming architecture to process real-time data events, allowing the client to monitor application logs and operational data in near real-time.Implemented data transformation workflows using AWS Glue, creating reusable ETL scripts and cataloging data assets for consistent, efficient data processing.Enhanced data quality and accuracy by deploying automated validation scripts in PySpark, enforcing data governance and reducing error rates.Built interactive Tableau dashboards, integrating Redshift data to support data-driven decision-making and visualize KPIs for the business.Deployed Infrastructure as Code (IaC) with CloudFormation, automating cloud resource provisioning for repeatable and consistent deployment environments.Documented project architecture, workflows, and operational guidelines, providing clear knowledge transfer to team members and stakeholders.