Data Engineer Power Platform

RITHESH RAJA

Master’s in computer science

*************@*****.*** • +1-217-***-**** • Charolette, NC • https://www.linkedin.com/in/rithesh-raja-14a65a167/ • https://github.com/ritheshr PROFESSIONAL SUMMARY

A proficient Power Platform Developer focusing on leveraging robust development and management capabilities to enhance organizational performance and scalability. Demonstrated success through an internship at the Illinois State Department of Central Management Services, successfully deploying live applications with strong analytical thinking, problem-solving, and effective communication skills. Experienced as a Google Cloud Platform Data Engineer, with project experience utilizing tools such as Apache Spark, Hadoop, Google Cloud, Apache Airflow, and Big Query, including deploying ETL data pipelines at Infinite Computer Solutions, as a full-time employee for 5+ years. Actively pursuing opportunities to contribute to tech-driven environments that foster continuous improvement and digital advancement.

PROFESSIONAL EXPERIENCE

Power Platform Developer (Michael Curry Summer Intern - 2024) Jun 2024 - Aug 2024 Illinois Department of Central Management Services I Springfield, Illinois

• Worked closely with the development team to understand project requirements and developed an effective user-friendly app.

• Developed security-based applications using Microsoft Power Platform tools and JavaScript language.

• Created, customized, and deployed Power Apps applications to meet specific business needs, including developing forms, workflows, and interfaces.

• Designed and implemented automated workflows using Power Automate to streamline repetitive tasks and improve operational efficiency.

• Utilized Power BI to develop and maintain interactive dashboards and reports that provide actionable insights and support data-driven decision-making.

• Integrated Power Platform solutions with various data sources and services, ensuring seamless data flow and connectivity across different systems.

• Test applications and workflows to identify and resolve issues, ensure meeting functionality, reliability, and performance standards, and report the results.

• Prepared and maintained comprehensive documentation for applications, workflows, and processes to facilitate future updates and user training.

Data Engineer Jul 2018 - Jun 2023

Infinite Computer Solutions Limited Bengaluru, India

• Designed and implemented scalable data architectures on Google Cloud Platform, including BigQuery, Cloud Storage, Cloud Pub/Sub, and Dataflow, to process and analyze large datasets efficiently.

• Developed ETL pipelines using Cloud Data Fusion and Apache Beam to extract, transform, and load data from various sources into BigQuery for real-time analytics and reporting.

• Optimized and managed BigQuery data models, ensuring performance tuning, efficient query execution, and cost optimization.

• Collaborated with data scientists, business analysts, and cross-functional teams to define data pipelines and help with data integration for machine learning models and business intelligence reporting.

• Built and maintained robust data models and data warehousing solutions on Google Cloud Storage and BigQuery, ensuring compliance with security and governance standards.

• Automated data workflows using Cloud Composer (Airflow) for scheduled and event-driven data pipeline execution.

• Implemented data security and access control mechanisms, leveraging Identity and Access Management (IAM) policies to ensure compliance with data privacy regulations.

• Created dashboards and reporting tools in Google Data Studio and Looker to visualize key metrics and provide insights to stakeholders.

• Monitored pipeline performance using Cloud Monitoring and Cloud Logging, ensuring high availability and minimizing downtime.

• Led data migration projects, transitioning on-premises data systems to GCP while ensuring minimal disruption and maintaining system integrity.

• Collaborated with DevOps teams to integrate data pipelines into CI/CD workflows for continuous deployment and integration.

• Provided ongoing support and troubleshooting for data pipelines, ensuring efficient data flow and resolving issues with performance, scalability, or data quality. TECHNICAL SKILLS Languages:

Python, JavaScript, Cascading Style Sheets CSS, HTML, SAS, Spark Database Languages:

SQL, MySQL, Big Query, MongoDB

Tools:

PowerApps, Microsoft SharePoint, Power BI, Power Automate, Apache Airflow, GCP, Google Big Query, Apache Spark, Hadoop, Git, GitHub

EDUCATION

Master’s in computer science Anticipated: May 2025 University of Illinois at Springfield, GPA: 4.0

PROJECTS

IRCTC DATA pipeline Data Engineering project Dec 2024 – F eb 2024

• Developed a Python-based Dataflow pipeline leveraging Apache Beam to read messages from Pub/Sub and apply transformation logic using a custom UDF (transform_udf.py) for data enrichment, validation, and standardization.

• Designed and implemented a Pub/Sub-based ingestion system to capture real-time mock IRCTC data.

• Registered and utilized custom transformation UDFs to clean and structure incoming data before storage.

• Ensured data governance, security, and compliance by implementing robust data quality checks and error-handling mechanisms.

• Stored and managed transformed data in Big Query for real-time querying and analytics.

• Improved data integrity and efficiency by optimizing data pipeline execution, reducing processing latency, and enhancing scalability.

Weather App Development: Oct 2024 – Dec 2024

• Developed a weather application that fetches real-time weather data from a third-party API, providing users with current weather conditions and forecasts.

• Created a visually appealing layout using CSS, incorporating icons to represent various weather conditions for enhanced user experience.

• Implemented a dynamic welcome page featuring login and sign-up options, ensuring a smooth user onboarding process.

• Ensured the application was fully responsive, providing an optimal user experience on both mobile and desktop devices through flexible layouts and media queries.

• Integrated visual effects that change backgrounds and animations based on weather conditions (e.g., rain, sun, storms) to create an immersive experience.

• Developed features to display weather-related metrics, including precipitation percentage, wind gusts, and temperature, enhancing user engagement.

• Added animations to various UI elements to create a dynamic and interactive user experience, improving overall app aesthetics and usability.

• Implemented performance optimizations to ensure fast loading times and smooth transitions, enhancing user satisfaction.

• Utilized HTML, CSS, and JavaScript for front-end development, with experience in API integration and responsive design principles.

Employee Salary Data Analysis and Visualization: Aug 2024 - Sep 2024

Imported a dataset of 10,291 employee records and performed data cleaning to handle missing values, resulting in a refined dataset of 10,258 entries.

Conducted statistical analysis to summarize key metrics (mean, median, standard deviation) for base salary, overtime pay, and longevity pay.

Developed histograms to visualize the distribution of base salary, overtime pay, and longevity pay, revealing right-skewed distributions.

Created pie charts to illustrate the proportion of employees across different salary and pay bins.

Designed 3D scatter plots to explore relationships between base salary and mean overtime/longevity pay.

Generated area charts to visualize mean longevity pay salary.

Identified trends indicating that most employees earn lower base salaries and overtime pay, with fewer in higher

pay brackets.

Observed a strong correlation between base salary and mean overtime/longevity pay, suggesting further analysis.

Proficient in Python, utilizing libraries such as Pandas for data manipulation, Matplotlib for data visualization, and NumPy for numerical analysis.

Disease Detection in Pomegranate Using Machine Learning Aug 2023 - May 2024

Developed a machine learning-based system for early detection of diseases in pomegranate fruits.

Utilized image processing techniques to segment fruit images and identify suspicious lesions.

Employed GLCM (Gray Level Co-occurrence Matrix) for extracting textural features from images.

Implemented a two-phase approach: training the model with labeled data and testing its accuracy on unseen samples.

Created a dataset consisting of 400 images, including 150 images of "fruit rot," 90 images of "scab," and 160 images of healthy fruits.

Utilized a Support Vector Machine (SVM) classifier to categorize images into healthy or diseased classes.

Achieved an accurate rate of 83% in disease detection, demonstrating the effectiveness of the machine learning model.

Developed a user-friendly web application for farmers to upload images and receive instant disease predictions.

Focused on enhancing the efficiency of disease detection methods to minimize economic losses for farmers.

Conducted extensive preprocessing steps, including image normalization and resizing, to prepare data for model training.

Employed deep learning techniques, specifically Convolutional Neural Networks (CNN), to improve classification accuracy.

Integrated feature extraction methods to enhance the model's ability to differentiate between healthy and diseased fruits.

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