Chitya Dyotha Rajendra

Data Engineer with 7+ years of experience designing scalable data pipelines, ETL solutions, and cloud-based data platforms. Experienced in Python, SQL, Scala, Apache Spark, Hadoop, Hive, Kafka, Talend, and Apache NiFi for building high-performance data processing solutions. Skilled in AWS and Azure, data modeling, ETL automation, and database technologies including SQL Server, PostgreSQL, MongoDB, and MySQL.

Proficient in Power BI, Tableau, and SSRS for analytics and reporting, with hands-on experience in Agile delivery and Python libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib to support data engineering and business intelligence initiatives.

Data Engineer II - Amazon - USA

(2025-09)

• Led the migration of Amazon's SPMS platform across 170+ teams, 10 pipelines, 33 datasets, and 10K+ dashboard users, ensuring a zero-defect production launch.
• Optimized SQL-based ETL pipelines and deduplication logic across 23+ consumer pipelines, improving reporting accuracy and saving 100+ engineering hours.
• Built cloud-native analytics infrastructure and AWS QuickSight dashboards supporting multiple GenAI products, delivering 30+ operational and business metrics for executive reporting and real-time application monitoring.
• Developed scalable data models, multi-table joins, metadata enrichment frameworks, and standardized analytics datasets using SQL and AWS services, improving AI-assisted analytics accuracy and enterprise data accessibility.
• Implemented robust data quality, validation, reconciliation, and monitoring frameworks by validating over 661K production records across 17+ quality checks, ensuring data integrity and production reliability.
• Collaborated with cross-functional teams on impact analysis, data lineage, schema design, and production deployments for enterprise data platforms.
• Authored technical documentation and implementation guides, improving deployment consistency and reducing production support efforts.
• Designed and operationalized serverless data pipelines (Spark/Cradle, S3, Andes) to ingest, transform, and serve LLM-generated outputs — including RAG response evaluations, intent classification accuracy, and thematic topic modeling — enabling real-time operational monitoring of an AI-powered chatbot serving 400K+ users.
• Built automated ETL pipelines to integrate LLM-as-judge accuracy metrics (intent correctness, data groundedness, response precision) with RAG telemetry data, enabling data-driven feedback loops between Science and Product teams to continuously improve retrieval quality and reduce hallucination rates in production AI responses.

Big Data Engineer - Wells Fargo - USA

(2024-04 - 2025-09)

• Collaborated with cross-functional Agile teams to define user stories and acceptance criteria, contributing to sprint planning and backlog grooming, ensuring on-time delivery of 100% of project milestones.
• Optimized data processing and automated ETL workflows using Talend, SSIS, and Hadoop ecosystem components, improving data flow efficiency by 30% and ensuring 99% data accuracy and integrity.
• Executed advanced data visualizations with Matplotlib and Seaborn, generating 50+ insightful charts and plots that enhanced data communication and informed key business decisions.
• Architected, deployed, and maintained scalable data pipelines on AWS, utilizing services such as S3, EC2, Lambda, and Glue, reducing data processing time by 40% and improving pipeline reliability.
• Designed and delivered 30+ custom reports using Tableau Desktop and SSRS, enabling stakeholders to extract actionable insights, improving data-driven decision-making by 25%.
• Managed cloud-based data architectures (AWS S3, Azure Data Lake, GCP Big Query) to enable scalable and cost-effective analytics.
• Automated data ingestion and transformation workflows using Airflow, reducing manual intervention by 50%.

Big Data Engineer - SunPharma Health - India

(2021-08 - 2023-07)

• Developed scalable and HIPAA-compliant data pipelines to process and integrate electronic health records (EHR), clinical trial data, and real-time patient monitoring data for predictive analytics and decision support.
• Led the development of a data lake leveraging Hadoop and Spark to aggregate and process clinical and operational data from multiple hospital systems.
• Optimized data processing workflows, improving data query performance by 30% and enabling real-time access to patient records and healthcare analytics.
• Collaborated with data scientists to deploy machine learning models for predicting patient readmissions, optimizing hospital resource utilization, and enhancing patient care.
• Implemented encryption and anonymization protocols to ensure the security and privacy of healthcare data, in compliance with HIPAA regulations.
• Supported migration of healthcare data infrastructure from on-premises to AWS Cloud, achieving 40% cost savings while improving system scalability.

Big Data Engineer - Miraicoders Technology - India

(2018-07 - 2021-08)

• Orchestrated a real-time streaming pipeline by integrating Azure Data Factory with Event hubs and Data bricks for transformations and ingested data into Azure SQL, cosmos DB, and ADLS.
• Integrated Azure Logic Apps and Azure Functions with Azure Monitor for real-time data pipeline monitoring and alerting, resulting in a 30% improvement in issue resolution time.
• Scheduled workflows in Apache Airflow, ensuring seamless transformation and resolving 100% issues checking Airflow tools, logs, and corrective actions.
• Developed REST APIs using Python, Fast API, SQL Alchemy, NumPy, and Pandas resulting in a 25% reduction in response time. Deployed applications on Azure VM using Azure DevOps, and Azure Kubernetes.
• Designed snowflake modeling in data warehousing, improving data cleansing by 25%, optimizing slowly changing dimensions, and enhancing change data capture and data validation.

Master of Science - Data Analytics Engineering - George Mason University (2025-05)

Bachelor of Engineering - Electronics and Communication - Bangalore Institute of Technology (2018-06)