Rajitha Thatiparthi

Designed and maintained cloud-native ETL/ELT pipelines using Python, SQL, Azure Data Factory, and Snowflake to support enterprise analytics and reporting use cases. Extracted, cleaned, and transformed large datasets from Azure Blob Storage and Snowflake, delivering analytics-ready tables used by multiple business and analytics teams. Built PySpark transformation pipelines to generate time-based metrics, aggregations, and anomaly indicators, processing large-scale datasets reliably. Supported machine-learning workflows by preparing feature datasets, validating training inputs, and ensuring data consistency using Python and Scikit-learn. Automated ingestion, transformation, and scheduled refresh workflows using Apache Airflow, improving pipeline reliability and reducing manual intervention. Integrated structured and semi-structured event data into Snowflake and Redshift, improving data availability and query performance for downstream analytics. Developed Python-based data validation frameworks to detect missing data, schema drift, and anomalies, strengthening data quality across analytical systems. Supported real-time data pipelines using Kafka and Spark Structured Streaming to deliver near-real-time analytical data. Collaborated with product, analytics, and engineering teams to gather requirements, define data logic, and document transformations and lineage. Created and maintained technical documentation to support onboarding, audit readiness, and operational transparency.

Built and optimized ETL/ELT pipelines using AWS Glue, Python, and Redshift SQL to deliver reliable datasets for analytics and reporting teams. Performed exploratory data analysis and root-cause investigations using Python (Pandas, NumPy) and optimized complex SQL queries to resolve data quality issues. Engineered aggregates, derived metrics, and statistical features to support analytical models and business dashboards. Prepared and validated machine-learning input datasets using Python and Scikit-learn, enabling stakeholders to evaluate and compare model performance. Implemented automated data validation checks for missing values, duplicates, and outliers, improving dataset reliability and reducing reporting errors. Developed dashboards using Tableau, Power BI, and Grafana to visualize trends and operational KPIs for business users. Supported data migration initiatives into AWS cloud environments and optimized pipeline runtime and throughput. Worked closely with cross-functional teams to translate analytical requirements into scalable pipeline logic using Spark-based processing. Documented transformation logic, data lineage, and operational procedures to ensure reproducibility and compliance. Participated in Agile delivery, supporting sprint planning, backlog refinement, and timely delivery of data assets.

Supported Telstra network operations by monitoring alarms, service degradation, and incident queues to ensure timely issue resolution. Performed L1 troubleshooting using standard runbooks, triaging network faults and escalating issues to L2/L3 teams with detailed diagnostics. Monitored network performance KPIs such as availability, latency, packet loss, and link utilization to identify anomalies and potential outages. Created and updated incident tickets with accurate timelines, troubleshooting steps, and resolution notes, ensuring adherence to operational SLAs. Coordinated with NOC, field teams, and service desks during incidents to validate restoration and confirm network stability. Prepared shift handover reports summarizing incidents, recurring issues, and follow-up actions to maintain operational continuity. Maintained operational documentation and knowledge articles to streamline troubleshooting and reduce repeat escalations.

Developed a project on Travel Insurance Claim Prediction utilizing machine learning models to enhance predictive accuracy and risk assessment. Designed and implemented an Advanced Java-based web application using JSP and Servlets for dynamic web functionality. Conducted research and authored a paper on Challenges of Homomorphic Encryption, analyzing its security implications and computational efficiency. A research paper on Reinforcement Learning for Robotics and Autonomous Systems, exploring its applications in decision-making and automation.