RAKESHKUMAR REDDY POREDDY

• Implemented data pipelines for large-scale healthcare data analysis using AWS EMR and Snowflake, enhancing data accessibility and analysis capabilities
• Developed predictive models to improve patient care outcomes, utilizing Python and SQL in conjunction with collaborative analytics tools
• Engineered real-time data streaming solutions with Apache Kafka and AWS S3 to facilitate instant access to healthcare data for timely decision-making
• Automated data processing workflows using Airflow, increasing operational efficiency and reducing manual errors in data handling
• Developed complex data pipelines using Databricks, optimizing data flow from multiple sources into structured and unstructured data lakes
• Leveraged Apache Spark on Databricks for large-scale data processing, achieving significant performance improvements and cost reductions
• Spearheaded the integration of PySpark with AWS services (S3, EMR, Redshift) to enhance data storage and retrieval processes
• Utilized PySpark with Kafka to build real-time data streaming applications, improving data availability and decision-making capabilities
• Implemented machine learning algorithms with PySpark MLlib to predict trends and behaviors from large datasets, enhancing business strategies
• Led projects that integrated data from various sources into MongoDB, enhancing data storage and retrieval processes for critical healthcare applications
• Utilized Python to create scripts that automated routine data cleaning and preparation tasks, streamlining the data pipeline
• Implemented data integration solutions using AWS Glue to consolidate disparate data sources, simplifying data management and improving data quality
• Enhanced data security and compliance in Databricks environments by implementing robust security measures and monitoring tools
• Trained and mentored junior data engineers and analysts on best practices and efficient use of Databricks in data engineering projects
• Collaborated with cross-functional teams to define data requirements and deliver scalable data solutions using Databricks
• Optimized SQL queries and database schemas in PostgreSQL and Teradata, significantly improving performance and scalability of healthcare data applications
• Deployed DBT for data transformation tasks within cloud environments, enabling more efficient data modeling and reporting
• Coordinated with clinical staff to leverage analytics in operational and patient care strategies, using Pandas to analyze and interpret data
• Configured and managed data streaming architectures using Apache Kafka, ensuring robust data flow and integration for real-time analytics
• Fostered a collaborative environment using Databricks Notebooks, facilitating sharing insights and working closely with data scientists to refine analytics models
• Implemented data governance practices using Databricks Unity Catalog, ensuring compliance with data security and privacy standards
• Conducted data migration projects to AWS EMR, ensuring seamless transitions with minimal downtime and no data loss
• Developed and maintained robust data security protocols using AWS technologies, safeguarding sensitive healthcare data against potential breaches
• Integrated machine learning algorithms with existing data systems using Python and Pandas, enhancing predictive analytics capabilities
• Enhanced data visualization and reporting capabilities using Python and SQL, enabling healthcare professionals to access tailored dashboards
• Automated various data processes using Python scripting, significantly reducing the time required for data preparation and loading
• Performed extensive data cleaning and normalization to improve the quality of healthcare analytics, using Python and SQL
• Architected a data lake solution utilizing AWS S3 as the primary storage, paired with AWS Glue and Athena for efficient serverless querying
• Enhanced data security for AWS S3 storage by implementing comprehensive bucket policies, IAM roles, and enforcing encryption standards both in transit and at rest
• Automated data transformation and cleansing processes using Python scripts and AWS Lambda, storing outputs in AWS S3 to ensure high-quality data for analytics
• Managed large datasets using MongoDB, optimizing data architecture for better performance and scalability in healthcare applications
• Integrated real-time and batch data processing using Apache Kafka and AWS EMR, balancing workload and improving processing time
• Deployed and optimized DBT models in cloud environments, enhancing data transformation and load processes for complex healthcare datasets
• Implemented automated monitoring with Prometheus and Grafana in CI/CD workflows, enabling proactive issue detection and resolution in data pipelines
• Utilized Ansible for configuration management, automating the setup and maintenance of Hadoop clusters integrated with CI/CD pipelines
• Architected a CI/CD strategy for a multi-cloud environment using Spinnaker, improving deployment strategies across AWS platform
• Assisted in the development of a collaborative analytics platform, facilitating data sharing and decision-making across healthcare teams
• Implemented robust backup and disaster recovery solutions using AWS technologies, ensuring high availability and data integrity
• Conducted performance tuning of healthcare databases in PostgreSQL and Teradata, ensuring optimal performance during critical data operations
• Streamlined data ingestion and integration using AWS Glue and Python, enhancing the speed and efficiency of data flows into healthcare analysis systems
• Environment: AWS EMR, Snowflake, Python, SQL, Databricks, Apache Kafka, AWS S3, Airflow, MongoDB, AWS Glue, PostgreSQL, Teradata, DBT, Pandas

• Automated data transformations using Terraform and Python, enhancing data consistency and reducing manual intervention across insurance datasets
• Improved data quality and integration using Informatica and Apache NiFi, which facilitated accurate reporting and analytics in financial services
• Implemented real-time data streaming solutions with Databricks Structured Streaming and Apache Kafka to enhance data availability and decision-making processes
• Leveraged Docker and Kubernetes to create reproducible environments for data pipelines, facilitating consistent deployments across development, testing, and production stages
• Developed Terraform scripts to automate the provisioning of cloud infrastructure on AWS, ensuring scalable and resilient data engineering solutions
• Led the migration of legacy data systems to Hadoop-based platforms, leveraging PySpark for efficient data transformation and aggregation
• Utilized Databricks SQL Analytics to provide actionable insights through dashboards and visual reports, enhancing business decision-making
• Led migration projects to Databricks, achieving seamless transitions from legacy systems and enabling cloud-based data analytics capabilities
• Utilized Databricks MLflow to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment of ML models
• Deployed machine learning models for insurance risk assessment using PyTorch, integrated with AWS Quick Sight for dynamic visualization
• Optimized costs by analyzing and reconfiguring data storage and retrieval practices in AWS S3, applying lifecycle policies to transition to cost-effective storage tiers
• Leveraged AWS S3 event notifications to automate and trigger downstream processing in AWS Lambda, enhancing responsiveness in event-driven data architectures
• Created a secure, scalable multi-tenant data storage framework using AWS S3, improving data access control, governance, and compliance with international standards
• Managed Agile project implementations, utilizing JIRA for project tracking, ensuring timely delivery of data enhancements and upgrades
• Developed cost-effective solutions on Databricks by optimizing cluster management and leveraging spot instances
• Ensured data security by implementing ACLs and role-based access control within Databricks environments
• Designed data pipelines that combined AWS Glue with Python scripting to automate and simplify data ingestion and integration processes
• Conducted data quality assessments using SQL and Python, ensuring high data integrity and supporting compliance with regulatory standards
• Optimized data storage and processing using AWS DynamoDB, improving performance and scalability for large insurance datasets
• Implemented version control best practices using Git, enhancing collaboration and code management across the data engineering team
• Enhanced data visualization capabilities using AWS Quick Sight, enabling stakeholders to derive actionable insights from complex datasets
• Streamlined data migration processes using Terraform, ensuring efficient and error-free transitions between different storage platforms
• Facilitated the integration of structured and unstructured data using Apache NiFi, supporting comprehensive analytics in insurance applications
• Developed real-time data feeds using Apache Kafka, enabling immediate data availability for timely decision-making in risk management
• Orchestrated data workflows using Informatica, automating complex transformations and loading processes in the insurance data environment
• Designed and implemented robust backup and disaster recovery strategies using AWS technologies, ensuring data availability and continuity
• Automated repetitive data processing tasks using Python, significantly improving efficiency and reducing processing times
• Configured AWS S3 for optimized data storage and retrieval, effectively managing large volumes of insurance transaction data
• Conducted performance tuning of AWS DynamoDB instances, optimizing response times and resource utilization for critical applications
• Implemented security measures in data handling and storage using AWS security tools, safeguarding sensitive insurance customer data
• Utilized Apache NiFi for efficient data ingestion and distribution, enhancing the flow of information across insurance processes
• Enhanced team collaboration and project management using Agile methodologies and JIRA, improving overall project visibility and tracking
• Developed and maintained documentation for data processes and systems using Confluence, ensuring knowledge sharing and continuity
• Supported the training of team members on new technologies and data processes, fostering a culture of continuous learning and improvement
• Environment: AWS Glue, AWS S3, AWS DynamoDB, Databricks, Terraform, Python, Informatica, Apache NiFi, PyTorch, AWS QuickSight, JIRA, SQL, Git, Apache Kafka, Confluence

• Designed data ingestion solutions using Apache NiFi, optimizing the flow of financial data for analytics purposes
• Configured AWS DynamoDB to provide scalable and reliable data storage solutions tailored for the financial sector
• Utilized Terraform to automate cloud infrastructure deployments, enhancing operational agility and system reliability
• Deployed data applications in Docker containers to improve scalability and manageability of financial services applications
• Developed Python scripts for data transformation, streamlining the processing tasks within the financial data environments
• Integrated AWS S3 for secure and scalable data storage, supporting extensive data management solutions in finance
• Employed Git for version control, ensuring the integrity and collaboration in the development of financial data projects
• Orchestrated automated data pipelines using Informatica, improving data quality and efficiency in data handling
• Designed and implemented robust data pipelines in Databricks, integrating Apache Spark for real-time data processing and analytics
• Skilled in using Databricks MLflow to manage machine learning lifecycle, including experimentation, reproducibility, and deployment
• Leveraged AWS QuickSight for developing insightful dashboards and visualizations, aiding financial decision-making processes
• Configured AWS DynamoDB for high-performance data operations, enhancing data retrieval and storage in financial applications
• Managed agile project cycles using JIRA, ensuring timely delivery of data engineering projects in the finance domain
• Implemented data validation and testing using PyTorch, ensuring accuracy and reliability of predictive models in finance
• Applied data encryption and security measures in AWS, ensuring compliance with financial regulations and data privacy standards
• Developed and maintained data warehouses using AWS technologies, facilitating complex financial analyses and reporting
• Implemented automated monitoring and alerting mechanisms in Databricks, ensuring high availability and performance of data processes
• Developed custom UDFs (User Defined Functions) in Databricks for specific business logic integration, enhancing data transformation capabilities
• Utilized Tableau for complex data visualization tasks, enhancing the presentation and accessibility of financial data
• Enhanced data workflows with Apache Kafka, improving the real-time data streaming capabilities in financial operations
• Configured automation frameworks using Terraform, streamlining infrastructure management for financial services
• Applied continuous integration and deployment practices using Docker, enhancing the reliability of financial data applications
• Utilized AWS DynamoDB streams to capture real-time changes in financial data, enhancing data accuracy and timeliness
• Orchestrated data migrations to AWS cloud environments, ensuring seamless transitions and minimal downtime
• Enhanced operational efficiency and data processing using AWS Glue, automating data integration tasks in the financial domain
• Environment: Apache NiFi, AWS Glue, DynamoDB, Terraform, Docker, Python, Git, Informatica, AWS S3, QuickSight, JIRA, PyTorch, Tableau, Apache Kafka, and AWS DynamoDB

• Analyzed data quality and implemented enhancements using Python and SQL, utilizing Apache Hive for big data management and insights
• Designed visualizations in QlikView and Tableau, facilitating insightful business reports and dashboards that drove strategic decisions
• Managed version control for data projects using Subversion (SVN), ensuring code integrity and facilitating collaborative development
• Supported business intelligence initiatives with robust data models using Power BI, enhancing reporting capabilities across departments
• Leveraged Apache Hadoop for efficient processing of large datasets, improving data access and analysis for client projects
• Developed and maintained data warehouses using SQL, ensuring structured data storage and efficient data retrieval
• Configured and utilized Apache Hive to handle complex data queries, optimizing data operations and analytics
• Implemented data governance and compliance measures, ensuring data integrity and security were maintained
• Conducted data visualization workshops using Tableau and QlikView, enhancing team skills and data presentation techniques
• Optimized data retrieval processes using custom SQL queries, reducing processing times and improving response rates
• Performed data migrations and integrations using Apache Hadoop, ensuring data consistency and accuracy
• Developed SQL scripts for database management and report generation, enhancing operational efficiency and data usability
• Utilized Power BI to develop interactive and automated reporting solutions, increasing data accessibility for non-technical users
• Assisted in database tuning and performance optimization, ensuring high performance and reliability of data operations
• Engaged in agile project management practices, utilizing JIRA to manage tasks and monitor project progress
• Delivered comprehensive data analysis reports to stakeholders, providing insights that influenced key business strategies
• Environment: Python, SQL, Apache Hive, QlikView, Tableau, Subversion (SVN), Power BI, Apache Hadoop, JIRA

• Developed SQL databases using MySQL and PostgreSQL, enhancing data operations for business analytics and reporting
• Utilized Talend for data integration and transformation, supporting analytics and business intelligence activities
• Managed data backups and recovery procedures using Git, ensuring data integrity and high availability
• Implemented dashboarding solutions with Power BI, delivering actionable insights to enhance business decision-making
• Conducted performance tuning on SQL databases to ensure optimal operation and access speeds
• Developed and maintained documentation for database designs and data management processes
• Designed and executed SQL queries for data analysis and reporting, supporting various business units
• Collaborated with business analysts to understand data requirements and deliver tailored database solutions
• Participated in data migration projects, ensuring seamless data transfers with minimal downtime
• Assisted in the setup and configuration of PostgreSQL databases, optimizing settings for performance and security
• Developed custom data extraction and reporting tools using Python, enhancing data accessibility and user engagement
• Trained junior developers in database management and ETL processes, fostering skill development within the team
• Monitored and resolved database performance issues, ensuring stable and efficient data operations
• Engaged in project meetings to provide updates on database health and data management strategies
• Environment: MySQL, PostgreSQL, Talend, Git, Power BI, Python

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