Jeevan Reddy Guduru

• Led a team of 7 data professionals, overseeing the delivery of multiple data initiatives with a focus on scalability, performance, and governance.
• Architected and implemented data pipelines to optimize cargo pricing strategies, leveraging AWS Lambda, Step Functions, EMR, and S3—resulting in a 75% reduction in data retrieval time.
• Led the development of an Azure-based ingestion framework to bring CRM data from on-premise SQL Server into Synapse Analytics using Azure Data Factory (ADF).
• Designed and automated data quality workflows for sea voyage contracts by integrating AWS Glue with Snowflake, reducing manual intervention by 70%.
• Built proactive monitoring and alerting systems using AWS Lambda and SES, enhancing pipeline reliability and reducing response time to data issues.
• Designed a high-performance data validation framework using Polars and concurrent processing to support multi-user execution and scale validation for large datasets (100MB+), improving execution speed and resource efficiency.
• Defined and enforced data governance policies and architectural standards, ensuring enterprise-wide data consistency, quality, and compliance.
• Promoted engineering best practices in version control and CI/CD using Git and GitHub, streamlining collaboration, code management, and deployment workflows.
• Fostered a culture of transparency and innovation, conducting regular team syncs, knowledge-sharing sessions, and cross-functional alignment reviews.

Spearheaded the end-to-end design, architecture, and deployment of a data lake on AWS, capturing user interaction data via Segment to power real-time analytics.

• Designed and implemented scalable data pipelines and governance frameworks, integrating with data catalogs to ensure discoverability and compliance.
• Engineered fraud detection pipelines and dashboards across multiple dating platforms, reducing fraud incidents by 80% and strengthening platform security.
• Developed Python-based data migration workflows from Athena to Snowflake, boosting data processing efficiency by 70% while ensuring data integrity.
• Orchestrated ETL pipelines using Apache Airflow, improving scheduling reliability and cutting execution times by 60%.
• Delivered interactive dashboards using Apache Superset with complex SQL logic, enabling real-time insights for business stakeholders.
• Tuned database and ETL performance, significantly reducing latency and enhancing responsiveness across analytics workflows.
• Berlin, Germany

• Architected and delivered a credit risk analytics platform in the financial sector, leveraging Azure Data Factory and Databricks to integrate customer, transaction, and demographic data for real-time risk scoring and predictive modeling.
• Led the design and implementation of scalable data pipelines to ingest customer transaction data from external sources while ensuring full compliance with data governance and security standards.
• Modernized and optimized legacy ETL workflows, migrating data from on-premises systems to AWS (Redshift), resulting in a 50% improvement in processing speed and system maintainability.
• Built automated test frameworks for system and integration validation, improving data quality and reducing error rates by 60%, supporting reliable decision-making.
• Partnered with cross-functional stakeholders to translate business requirements into robust technical solutions, ensuring delivery aligned with strategic data objectives.
• Developed a comprehensive data architecture blueprint to support scalable analytics and reporting systems, aligned with organizational goals.
• Created logical and physical data models to define enterprise data structure, relationships, and lineage, enabling efficient access and analytics across teams.