Machine Learning Big Data

Sai Reddy Peram

810-***-**** **************@*****.*** Mt Pleasant, MI https://www.linkedin.com/in/sai-reddy-peram PROFESSIONAL SUMMARY

Experienced Data Governance Analyst with 5 years of expertise at MJR Builders, focused on harnessing big data to enhance market analysis, property valuation, and financial performance. Skilled in predictive modeling, machine learning, and data visualization, driving data-driven strategies that optimize operations and decision-making. Successfully led the development of real- time property valuation models, rental pricing optimization, and predictive maintenance systems, significantly improving efficiency and reducing costs. Eager to contribute advanced data engineering skills to innovative and impactful projects.

EDUCATION

Master’s in computer science Central Michigan University, Mt Pleasant GPA - 3.73 Aug 2022 - May 2024 Academic Specializations: Big Data Analytics, Data Engineering, Artificial Intelligence, Database Management Systems, Machine Learning

SKILLS

Technical:

Predictive Modeling: ARIMA, Prophet, Regression, Ensemble Learning Machine Learning: K-means Clustering, Collaborative Filtering, Logistic Regression, Decision Trees Data Visualization: PowerBI, Tableau, Seaborn, Kibana, Matplotlib Programming Languages: Java, Python, Scala, Spark, SQL Big Data & QL: MySQL, MongoDB, Hadoop, Apache Spark, Hive, PySpark, Scala Spark Cloud Services: AWS (EC2, KINESIS, EMR, RDS, S3, SNS, Glue, Athena), Azure Data Bricks, Google BigQuery Tools: Collibra, Informatica, Spark, Scala, Lombok, Postman, GIT, ETL, Scrum, PyCharm, Linux, PowerShell EXPERIENCE

MJR Builders Apr 2017 - July 2022

Data Governance Analyst:

• Developed and managed scalable ETL pipelines using AWS Glue, leading to a 50% reduction in data processing time for integrating construction site data from multiple sources into a unified data lake in AWS S3.

• Integrated Collibra with AWS Redshift and AWS Glue to automate data cataloging, lineage tracking, and data quality checks to certify reports and data sets (90%)

• Establishing data quality metrics using AWS CloudWatch and AWS Lambda for monitoring and alerts, further enhancing data reliability. Providing training and support to team members on utilizing Collibra for data governance and management tasks.

• Automating data quality checks and validations, ensuring consistency and accuracy across multiple data sources.

• Created dashboards and reports within Collibra to monitor data quality metrics and highlight areas for improvement.

• Collaborated with cross-functional teams to address data quality issues and implement corrective actions. Developed automated workflows in Collibra to manage data access and usage policies, ensuring compliance with industry regulations.

• Leveraged AWS Redshift to build and maintain a centralized data warehouse, optimizing data queries and enabling faster access to historical and real-time data. This resulted in a 60% improvement in reporting efficiency across multiple departments.

• Played a key role in ensuring timely material deliveries, minimizing project delays. Developed a dynamic resource allocation platform utilizing Apache Flink for real-time project needs and workforce availability.

• Implemented a cost-effective data archival solution using Amazon S3 Glacier, reducing long-term data storage costs by 40% while ensuring compliance with industry regulations.

• Collaborated with UI/UX designers to ensure the visualizations were both functional and aesthetically pleasing.

PROJECTS

Disaster Management System Python, Elastic Search, Kibana, Keras, TensorFlow, CNN

• Built a Disaster Management System using Python, Elastic Search, and Kibana, incorporating machine learning techniques with Keras, TensorFlow, and Convolutional Neural Networks (CNN).

• Aggregated weather-related data from sources including NOAA and OCNA, processing over 90,000 records per dataset. Developed an alert system for efficient disaster management, reducing response times by 30%.

• Implemented advanced data analysis and visualization techniques using Elastic Search and Kibana, contributing to the identification and monitoring of potential disaster situations. Real-Time ETL of Tweets using Big Data Tools and Cyberbullying Tweepy, Streamlit. S3, RDS, Kinesis, Transformers, Matplotlib, Cassandra

• Built an application that extracts real-time tweets (10,000) based on user-entered keywords and retrieves historical tweets from various datasets, storing them in S3. Integrated real-time and historical data using RDS for seamless display

• Used NoSQL Database DynamoDB for efficient data storage, enhancing system response times by 40%.

• Developed an alert system for cyberbullying detection, increasing content moderation accuracy by 25%. Master Data Management (MDM) with Collibra

• Implemented a master data management solution using Collibra integrated with AWS Glue and AWS Redshift to centralize and standardize key business data..

• Defined data stewardship roles and established data harmonization processes, improving data consistency and usability across the organization using AWS DataBrew for data preparation.

• Established data harmonization and enrichment processes, improving data consistency and usability across the organization.

• Monitored and managed data lifecycle processes within Collibra, ensuring timely updates and maintenance of master data.

• Established data harmonization and enrichment processes to enhance data consistency and usability across the organization.

Energy Efficiency Analytics for Construction Sites

• Led the development of a big data analytics platform monitoring energy efficiency on construction sites. Integrated IoT sensor data via AWS IoT Core, weather forecasts with AWS Lambda, and energy consumption records stored in AWS S3.

• Implemented machine learning algorithms to predict optimal energy consumption patterns, resulting in a 20% reduction in energy costs and lowering environmental impact. Construction Analytics Dashboard in Tableau

• Designed and deployed a comprehensive Tableau dashboard hosted on AWS, providing real-time insights into construction project progress and financial performance. Increased decision-making efficiency by 50%.

• Used AWS EMR with Apache Spark for data processing and AWS Quick Sight for visualization.

• Developed an automated ETL process using AWS Glue and AWS Lambda to extract, transform, and load construction project data into AWS RDS. Integrated this data with Tableau for dynamic reporting, reducing manual data handling time by 60% and enhancing data accuracy by 45%. ADDITIONAL SKILLS

Communication: Fluent in English, Spanish, Japanese, Certifications: AWS Data Engineer-Associate, Tableau Desktop Specialist, Collibra Data Steward VLP

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