Data Engineer Senior
Resume:
Ramesh Ravula
***************@*****.***
https://www.linkedin.com/in/r-ramesh-492a15346/
Senior Data Engineer
PROFESSIONAL SUMMARY:
Over 9+ years of experience as a Senior Data Engineer, specializing in data integration, management, and analytics.
Expert in designing scalable data solutions using Apache Airflow, Python, and SQL, enhancing business operations efficiency.
Proficient in managing data migrations and integrating services with AWS, utilizing AWS Data Pipeline and AWS Glue for cloud data services.
Skilled in real-time data processing with Apache Kafka and Apache Flink, optimizing streaming and batch data processing workflows.
Developed robust data warehousing solutions leveraging Amazon Redshift and AWS S3, ensuring data availability and scalability.
Experienced in database management and query optimization with MySQL and PostgreSQL, enhancing data retrieval and storage operations.
Advanced user of Python for scripting and automation, improving the reliability and efficiency of data pipelines.
Utilized Google BigQuery and Google Cloud Storage for managing large datasets and performing complex queries in cloud environments.
Designed and implemented data visualization dashboards using Power BI, Tableau, and QlikView to provide actionable business insights.
Implemented machine learning models with TensorFlow and PyTorch to automate data-driven decision-making processes.
Managed and optimized ETL processes with Talend and Apache NiFi, improving data quality and processing time.
Deployed secure and compliant data solutions in the healthcare sector using HIPAA-compliant practices and technologies.
Enhanced data security and governance using Terraform for infrastructure as code to manage cloud resources securely.
Utilized Docker for containerization, ensuring consistency across development, testing, and production environments.
Employed Git for version control, enhancing team collaboration and maintaining a high standard of code integrity.
Architected microservices-based applications using AWS Lambda and API Gateway to create scalable and efficient service-oriented architectures.
Conducted data integrity and regression testing using Python to ensure the accuracy and reliability of data outputs.
Designed data integration frameworks using SSIS and Informatica, facilitating smooth data transfers and integrations across systems.
Experienced in Agile and Scrum methodologies, using Jira for project management and sprint planning to meet deadlines effectively.
Utilized Apache Hadoop and HDFS for managing and processing big data, enhancing data analysis capabilities.
Developed and managed APIs using Python and Flask for data manipulation and retrieval, improving system interoperability.
Implemented robust backup and disaster recovery solutions using AWS S3 and Redshift snapshots, ensuring data durability.
Performed complex data transformations and analytics using SQL and Python, providing deep insights into business metrics.
Utilized Apache Airflow for orchestrating and scheduling complex data workflows, automating routine data operations.
Implemented logging and monitoring using AWS CloudWatch, ensuring high availability and performance of data services.
Led data-driven projects, collaborating closely with cross-functional teams to align data initiatives with organizational goals.
TECHNICAL SKILLS:
Data Engineering Tools:
Apache Airflow, Talend, SSIS, Informatica, AWS Data Pipeline, Apache NiFi
Programming:
Python, SQL
Database Management:
MySQL, PostgreSQL, AWS Redshift, Google BigQuery
Data Visualization:
Power BI, QlikView, Tableau, AWS QuickSight
Version Control:
Git, Subversion (SVN)
Cloud Platforms:
AWS, Google Cloud, Azure
Machine Learning:
TensorFlow, PyTorch
Scripting and Automation:
Python, Terraform
Project Management:
Agile, Jira
Containerization:
Docker
Big Data Technologies:
Apache Hadoop, Apache Hive, HDFS
Real-Time Processing:
Apache Kafka, Apache Flink
Security and Compliance:
Data governance, regulatory compliance
PROFESSIONAL EXPERIENCE:
Client: Premier Inc, Charlotte, NC Feb 2023 to till date
Role: Sr Data Engineer
Roles & Responsibilities:
Designed and maintained scalable data pipelines utilizing Apache Airflow and Apache Kafka to enhance healthcare data operations.
Implemented secure data storage solutions on Google Cloud Storage and managed complex datasets using Google BigQuery.
Developed predictive analytics models using TensorFlow and Google AI Platform to improve healthcare outcomes.
Led end-to-end data analysis and governance efforts, ensuring data accuracy, consistency, and compliance across the Data Lake and Data Warehouse ecosystems.
Designed and optimized data workflows using Azure Data Factory, Synapse, and Power BI, enabling scalable analytics solutions and impactful business insights.
Collaborated cross-functionally with stakeholders and architects to gather business requirements, define metadata, and translate functional needs into technical specifications.
Optimized data streaming processes with Google BigQuery and Apache Kafka, enhancing real-time data analysis capabilities.
Created automation scripts to enhance ETL processes and data flow efficiency, leveraging Python and SQL.
Implemented robust security measures and compliance protocols to safeguard sensitive healthcare data, using tools like Terraform.
Collaborated closely with cross-functional teams to align data solutions with strategic healthcare business needs, employing Agile methodologies.
Performed complex data manipulation and analysis using SQL and Python to support decision-making processes.
Engineered robust data architectures to support enterprise data warehousing, utilizing HDFS and Apache Hadoop.
Utilized Docker and Terraform for environment management and deployment, ensuring consistent and reliable data operations.
Streamlined data ingestion and transformation processes using Snowflake, managing and analyzing healthcare data efficiently.
Optimized data retrieval and processing using HDFS and Apache Hadoop, enhancing data accessibility and reliability.
Enhanced data quality and reliability through rigorous testing and validation, employing automated tools and scripts.
Supported real-time data analytics and decision-making processes, providing actionable insights to healthcare professionals.
Maintained high availability and performance of data systems to ensure uninterrupted service in the healthcare sector.
Continuously monitored and improved data system performance, using AWS CloudWatch and custom Python scripts.
Developed and maintained documentation for data systems and procedures, ensuring clarity and compliance in operations.
Trained team members on new technologies and best practices, enhancing skill development within the data team.
Implemented data governance and metadata management practices, ensuring data integrity and effective data utilization.
Designed solutions for data redundancy, recovery, and failover strategies, enhancing system resilience and reliability.
Led initiatives to integrate new data sources into the existing ecosystem, expanding data capabilities and insights.
Evaluated and adopted new tools and technologies to improve data processing and analytics capabilities continuously.
Ensured compliance with healthcare regulations and data privacy laws, maintaining high standards of data security.
Developed APIs for data access and integration with other systems, enhancing interoperability and data sharing.
Fostered a culture of innovation and continuous improvement within the data team, driving advancements in data practices.
Environment: Apache Airflow, Apache Kafka, Google Cloud Storage, Google BigQuery, TensorFlow, Google AI Platform, Python, SQL, Terraform, Agile methodologies, HDFS, Apache Hadoop, Docker, Snowflake, AWS CloudWatch.
Client: Comcast, Philadelphia, PA Feb 2021 to Jan 2023
Role: Data Engineer
Roles & Responsibilities:
Architected and maintained ETL pipelines using Informatica and AWS Glue to streamline media content data management.
Managed large-scale data warehouses using AWS Redshift and AWS S3, ensuring efficient data storage and retrieval.
Designed data integrations and workflows with Apache NiFi and Informatica, improving data synchronization across platforms.
Developed automation frameworks for data processing and analysis, leveraging Python and SQL to increase efficiency.
Deployed machine learning models using PyTorch to enhance content recommendation systems and viewership predictions.
Optimized data visualization dashboards using Tableau and AWS QuickSight, providing actionable insights into viewer behavior.
Engineered solutions for data security and integrity in a cloud environment, utilizing AWS IAM and Security Groups.
Implemented agile practices using Jira to streamline project delivery and enhance collaboration across teams.
Utilized Git for version control in collaborative development projects, ensuring code consistency and quality.
Employed Docker for the containerization of data applications, improving deployment consistency across environments.
Developed Terraform scripts for automated cloud resource management, enhancing infrastructure as code practices.
Enhanced data quality through rigorous testing and validation processes, using automated testing tools and SQL queries.
Supported data-driven decision-making processes across departments, integrating business intelligence tools with operational systems.
Led cross-functional teams in data-driven projects and initiatives, fostering a collaborative and innovative work environment.
Documented technical procedures and data standards for team reference, ensuring best practices in data handling.
Provided technical support and training to team members on new tools and technologies, elevating team competencies.
Conducted performance tuning of data processes to ensure efficiency and scalability, using AWS CloudWatch and custom Python scripts.
Managed data compliance and governance according to industry standards, ensuring adherence to data protection laws.
Collaborated with stakeholders to define data requirements and goals, aligning data projects with business objectives.
Enhanced operational efficiency through automation and process improvements, applying Apache NiFi and AWS Lambda.
Automated data migration and integration tasks using AWS Data Pipeline and AWS Glue, reducing manual intervention.
Developed and maintained APIs using Python Flask for seamless data access and integration, enhancing system interoperability.
Facilitated knowledge sharing and continuous learning sessions on emerging technologies and data practices, using internal workshops and seminars.
Environment: Informatica, AWS Glue, AWS Redshift, AWS S3, Apache NiFi, Python, SQL, PyTorch, Tableau, AWS QuickSight, AWS IAM, Security Groups, Jira, Git, Docker, Terraform, AWS CloudWatch, AWS Data Pipeline, Python Flask.
Client: Northern Trust Bank, Chicago, IL. Oct 2018 to Feb 2021
Role: ETL Engineer
Roles & Responsibilities:
Developed and managed ETL workflows using SSIS and AWS Data Pipeline, improving data integration for financial reporting.
Implemented real-time data processing solutions with Apache Flink, enhancing transaction processing and analytics capabilities.
Managed cloud-based data storage solutions with AWS S3 and AWS Redshift, ensuring scalable and secure data handling.
Deployed data pipelines on AWS EMR for scalable processing of financial transactions and customer data.
Automated infrastructure management using Terraform, streamlining cloud resource provisioning and management.
Utilized Docker to create consistent deployment environments for data applications, enhancing operational reliability.
Optimized SQL queries and database performance for financial data on AWS Redshift, improving response times and data access.
Ensured data security and compliance with financial regulations using AWS IAM and encryption services.
Streamlined data integration processes with Apache NiFi, facilitating seamless data flows between internal and external systems.
Supported data analytics and reporting for financial insights using Python and SQL, aiding strategic decision-making.
Managed version control with Git, enhancing team collaboration and code quality across data projects.
Collaborated with IT and business teams to meet evolving data requirements, ensuring alignment with business goals.
Provided technical leadership and guidance to the data engineering team, fostering professional growth and innovation.
Developed documentation for data architectures and workflows, ensuring clarity and consistency in data operations.
Enhanced data accuracy and reliability through continuous testing and validation using Python scripts.
Led initiatives to improve data processing efficiency using Apache Airflow, reducing cycle times and resource usage.
Conducted performance tuning of data infrastructure using AWS CloudWatch and custom monitoring tools.
Integrated data security practices into all phases of data handling, ensuring compliance with stringent financial standards.
Facilitated the migration of legacy data systems to cloud-based platforms, using AWS services to modernize infrastructure.
Developed APIs for secure data access and integration using Python Flask, improving data interoperability across platforms.
Environment: SSIS, AWS Data Pipeline, Apache Flink, AWS S3, AWS Redshift, AWS EMR, Terraform, Docker, SQL (AWS Redshift), AWS IAM, Apache NiFi, Python, SQL, Git, Apache Airflow, AWS CloudWatch, AWS services, Python Flask.
Client: High Radius Technologies, Hyderabad, India Dec 2016 to Jun 2018
Role: Data Quality Analyst
Roles& Responsibilities:
Analyzed and enhanced data quality using SQL and Python, improving accuracy and reliability for client reports.
Designed and maintained data visualization dashboards in QlikView and Tableau, offering insights into business operations.
Managed data repositories using Apache Hive and Apache Hadoop, optimizing data storage and processing capabilities.
Conducted complex data analyses using Python and SQL, driving strategic business decisions for clients.
Implemented data governance practices with Apache Hadoop, ensuring data integrity and compliance with industry standards.
Optimized data workflows for efficiency using Python scripts, reducing processing times and operational costs.
Developed and tested SQL scripts for data manipulation and reporting, enhancing data accessibility for business analysts.
Collaborated with business analysts to understand and translate data needs into technical solutions, improving service delivery.
Maintained version control using Subversion (SVN) to manage the codebase, ensuring consistency across project developments.
Provided insightful data visualizations to clients through professional dashboards using Power BI and Tableau.
Assisted in the training and development of junior data analysts, enhancing team capabilities and knowledge sharing.
Enhanced business intelligence capabilities using Power BI, enabling more effective data-driven decision-making processes.
Documented technical specifications and project reports, ensuring clear communication and project documentation.
Supported client data queries and reports, delivering timely and accurate information for business operations.
Analyzed customer data to identify trends and insights, utilizing QlikView to provide actionable recommendations.
Streamlined data extraction and loading processes, leveraging Apache Hive for efficient data handling.
Developed customized SQL queries to address specific business challenges, improving client data interaction.
Ensured data security and privacy compliance, adhering to best practices and regulations in data management.
Environment: SQL, Python, QlikView, Tableau, Apache Hive, Apache Hadoop, Subversion (SVN), Power BI.
Client: Eclerx Service LTD, Pune, India May 2015 to Dec 2016
Role: SQL Developer
Roles & Responsibilities:
Developed complex SQL queries for data analysis and reporting, supporting business operations and client requirements.
Created data integration solutions using Talend, enhancing data processing efficiency and accuracy.
Managed MySQL and PostgreSQL databases, ensuring robust data storage and retrieval mechanisms.
Automated data pipelines using Python, streamlining data workflows and reducing manual intervention.
Developed business intelligence dashboards in Power BI, providing clients with clear insights into business metrics.
Implemented version control with Git, managing changes and updates to project codebases effectively.
Optimized data extraction and loading processes using Apache Airflow, improving data handling efficiency.
Supported data-driven decision-making processes in business operations, providing strategic data insights.
Conducted data quality checks using SQL to ensure the accuracy and reliability of client data.
Documented processes and results for technical reviews and audits, maintaining high standards of data governance.
Collaborated with development teams to integrate data solutions with client systems, enhancing operational efficiency.
Provided technical support for data issues, resolving challenges swiftly to maintain project timelines.
Enhanced data visualization capabilities using Power BI, facilitating better user engagement and understanding.
Developed and maintained ETL scripts in Python, ensuring timely data updates and transformations.
Assisted in migrating data to new systems, ensuring seamless transitions and minimal downtime.
Environment: SQL, Talend, MySQL, PostgreSQL, Python, Power BI, Git, Apache Airflow.
Education:
Bachelor of Technology (B.Tech) in Computer Science from JNTUH, Hyderabad, Telangana, India. - 2015
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