Data Analysis Azure Sql

Full Name: Vijaya Sree Vignatha Vangala

Email Id: ***************@*****.***

Phone Number: 513-***-****

https://www.linkedin.com/in/vvignathav/

Professional Summary:

Proficient in developing and implementing data ingestion and curation processes using Azure SQL Synapse, Cosmos DB, Data Factory, and Spark (Scala/Python).

Demonstrated success in performing performance tuning on Azure SQL Synapse and Cosmos SQL APIs to optimize data loading and consumption.

Expertise in utilizing Spark and Scala/Python for advanced data processing, leveraging Azure cloud services for scalability and efficiency.

Proven track record in creating and maintaining Delta Lake architectures within Azure Databricks to ensure data consistency and reliability.

Hands-on experience in conducting data analysis and anomaly detection using SQL, with results visualized through Power BI and Tableau.

Skilled in automating data processing workflows using shell/bash and PowerShell scripts, integrated with Azure services.

Proficient in collaborating with business stakeholders to gather and translate data requirements into scalable solutions using Azure Data Factory.

Expertise in designing and implementing CI/CD pipelines using Azure DevOps for continuous integration and deployment of data solutions.

Proven ability to manage the integration of external data sources into Azure ecosystems, ensuring seamless data flow and consistency.

Demonstrated experience in utilizing Git/GitHub for version control, ensuring smooth collaboration and tracking of code changes across teams.

Hands-on experience in building and optimizing complex T-SQL stored procedures to support reporting and data analysis with AWS QuickSight.

Skilled in developing and maintaining Microsoft Access databases and SSIS packages, integrated with AWS RDS for scalable management.

Expertise in reviewing and improving existing data processes through the integration of AWS Glue for enhanced efficiency and automation.

Proven success in supporting TAS business units by designing, creating, and testing reports using SQL, SSRS, Tableau, and Excel.

Proficient in managing project workflows using JIRA, ensuring timely delivery of tasks and effective collaboration.

Demonstrated ability to implement monitoring and alerting mechanisms using AWS CloudWatch to ensure the health and performance of data pipelines.

Expertise in designing and deploying CI/CD pipelines using Jenkins and GitHub for automated deployment processes in analytics solutions.

Skilled in conducting performance tuning and optimization on AWS Redshift queries, enhancing data retrieval efficiency.

Proven track record in implementing and maintaining ETL processes using AWS Glue to manage data ingestion and transformation.

Hands-on experience in designing and implementing data lakes on AWS S3 for large-scale data storage solutions.

Demonstrated success in developing data migration strategies to transition legacy systems to modern AWS data architectures.

Expertise in implementing Zero Trust security models within AWS to enhance data protection and minimize risks in data management.

Proficient in utilizing advanced SQL and data analysis skills to support business-critical applications, with visualizations in Tableau.

Proven ability to manage and optimize data governance frameworks, ensuring compliance and data quality within AWS cloud environments.

Hands-on experience in supporting cross-functional teams with data-driven decision-making processes, visualized through Tableau and AWS QuickSight.

Education:

Bachelors in Electronics and Communication Engineering, BVRIT HYDERABAD College of Engineering for Women - 2022

Masters in Information Technology, University of Cincinnati – 2023

TECHNICAL SKILLS:

Operating Systems

Unix, Linux, Mac OS, Windows

Programming Languages

Python, Scala, SQL, T-SQL, Shell Scripting, Bash, PowerShell, VBA

Data Processing & ETL

Azure Data Factory, Azure Databricks, AWS Glue, SSIS, Delta Lake, Spark (Scala/Python), Hadoop

Cloud Platforms & Services

Azure (SQL Synapse, Cosmos DB, Data Factory, Databricks, Delta Lake), AWS (Redshift, S3, RDS, Lambda, Glue, CloudWatch, QuickSight)

Databases

Azure SQL Synapse, Cosmos DB, AWS Redshift, AWS RDS, SQL Server, MySQL, PostgreSQL, MongoDB, Cassandra, Microsoft Access

Data Analysis & Analytics

SQL, T-SQL, Pandas, NumPy, Statistics Models, Data Visualization, Power BI, Tableau, AWS QuickSight

CI/CD & DevOps

Azure DevOps, Jenkins, Git, GitHub, CI/CD Pipelines, Version Control, JIRA

Data Governance & Security

Data Quality Assurance, Data Anomaly Detection, Data Governance Frameworks, Zero Trust Security Model

Big Data Technologies

Spark (Scala/Python), Hadoop, Delta Lake, AWS Glue, Redshift, Azure Synapse

Reporting & Visualization

Tableau, Power BI, AWS QuickSight, SSRS, Excel

IDE

IntelliJ, Eclipse, Visual Studio, IDLE

Project Management

JIRA, Azure DevOps, Confluence, Agile, Scrum, CI/CD Pipelines

Data Integration

Azure Data Factory, AWS Glue, SSIS, Delta Lake, Spark (Scala/Python), Shell/Bash Scripting

Performance Tuning

Azure SQL Synapse, Cosmos DB, AWS Redshift, Spark, T-SQL Optimization

Security & Compliance

Zero Trust Security Model, Data Governance, Data Quality Assurance, Access Controls

Professional Experience:

MetLife– Remote, United States Aug 2023 – Present

Technical Data Analyst

Responsibilities:

Developed and implemented data ingestion and curation processes using Azure SQL Synapse, Cosmos DB, Data Factory, and Spark (Scala/Python) to manage and process large datasets.

Performed performance tuning on Azure SQL Synapse dedicated SQL Pools, server SQL Pools, and Cosmos SQL APIs to optimize data loading and consumption processes.

Utilized Spark and Scala/Python to enhance data processing capabilities, leveraging Azure cloud services for scalability and efficiency.

Created and maintained Delta Lake architecture within Azure Databricks to ensure data consistency and reliability.

Conducted data analysis using SQL to detect anomalies and ensure data quality, with results visualized in Power BI and Tableau.

Automated data processing workflows using shell/bash and PowerShell scripts, integrated with Azure services.

Collaborated with business stakeholders to gather data requirements and translate them into scalable, efficient data solutions using Azure Data Factory.

Managed the integration of external data sources into the Azure ecosystem, ensuring seamless data flow and consistency.

Designed and implemented CI/CD pipelines using Azure DevOps for continuous integration and deployment of data solutions.

Conducted regular performance audits on data processes, optimizing them using Spark and Azure best practices.

Developed custom data transformations in Azure Data Factory to cleanse and enrich data, enhancing its usability for downstream applications.

Worked closely with data governance teams to ensure compliance with data quality standards across all data ingestion and curation processes.

Utilized Git/GitHub for version control, ensuring smooth collaboration and tracking of code changes.

Participated in the design and architecture of data models to support advanced analytics and machine learning applications within Azure.

Provided technical support and documentation for all data processes and scripts, ensuring smooth handover and maintenance by other teams.

Developed and implemented NLP models using Python and TensorFlow to analyze customer interactions, improving data-driven insights for customer service enhancements.

Leveraged Azure Machine Learning for model training, validation, and deployment, ensuring scalable and efficient AI/ML operations within the Azure ecosystem.

Environment: Azure SQL Synapse, Cosmos DB, Azure Data Factory, Azure Databricks, Delta Lake, Spark (Scala/Python), Power BI, Tableau, SQL, Shell Scripting, Bash, PowerShell, Git, GitHub, Azure DevOps, JIRA, Agile, SQL Server, Data Governance, Data Quality Assurance, Performance Tuning.

University of Cincinnati, Cincinnati, Ohio Oct 2022 – Jul 2023

Analytics- Data Analyst

Responsibilities:

Collaborated with university departments to define and scope reporting, analysis, and website performance enhancement requirements, utilizing AWS cloud services.

Designed, created, and tested reports using SQL, SSRS, Tableau, and Excel, ensuring they met business needs and provided actionable insights related to student engagement and website traffic.

Developed and maintained Microsoft Access databases and SSIS packages for data processing tasks, with integration to AWS RDS for scalable management of web analytics data.

Built and optimized complex T-SQL stored procedures to support reporting and data analysis on website traffic and student behavior, visualized through Tableau and AWS QuickSight dashboards.

Reviewed and improved data processes for tracking website performance, enhancing efficiency and automation by integrating AWS Glue for ETL operations involving site traffic data.

Provided day-to-day support for internal stakeholders and university faculty using website analytics applications, ensuring seamless performance within the AWS cloud environment.

Automated data processes using AWS Lambda and AWS Glue to streamline reporting and analytics tasks related to student interactions and web page usage.

Monitored and audited data quality for website usage metrics, ensuring accuracy and consistency across all processes using AWS Redshift.

Supported fellow analysts and university marketing teams in ongoing analytics projects, providing insights into website performance through Tableau dashboards integrated with AWS data sources.

Managed project workflows using JIRA, ensuring all website analytics tasks were tracked and completed on time.

Created and maintained complex data models in AWS Redshift to support advanced analytics on student engagement, page views, and conversion rates.

Implemented monitoring and alerting mechanisms using AWS CloudWatch to ensure the health and performance of data pipelines associated with the university website.

Designed and implemented LLM-based solutions using Python and AWS SageMaker to analyze vast amounts of unstructured web logs and feedback from students, improving the university's ability to optimize the website for better user experience.

Documented business requirements and translated them into technical specifications for development, ensuring alignment with AWS services and data governance standards.

Utilized TensorFlow and PyTorch for developing advanced machine learning models, enhancing predictive analytics capabilities for student behavior and retention patterns based on website usage data.

Communicated complex technical concepts effectively to non-technical stakeholders, using Power BI, Tableau, and AWS QuickSight to visualize key website metrics and trends.

Used Git/GitHub for version control, enabling effective collaboration and code management across the team.

Environment: AWS (Redshift, S3, RDS, Lambda, Glue, CloudWatch, QuickSight), SQL, T-SQL, SSIS, SSRS, Tableau, Excel, Microsoft Access, VBA, JIRA, Git, GitHub, Agile, Power BI, AWS DevOps, CI/CD Pipelines, Data Governance, Performance Tuning.

Deloitte, Hyderabad, India Sept 2021 – Jul 2022

Data Analyst

Responsibilities:

Designed and implemented data structures using ETL development on AWS, supporting data engineering, migrations, and pipelining.

Developed and maintained data governance frameworks and strategies to ensure data quality and compliance, utilizing AWS cloud services.

•Implemented Zero Trust security models within AWS to enhance data protection and minimize risks in data management.

•Worked on AWS Redshift and S3 to design scalable and secure data pipelines and storage solutions.

•Developed data migration strategies to transition legacy systems to modern AWS data architectures.

•Collaborated with cross-functional teams to support data-driven decision-making processes, visualized using Tableau.

•Ensured the integrity and security of data through adherence to best practices in data governance and AWS strategies.

•Provided insights and recommendations on data-related challenges, contributing to risk management and financial advisory efforts within AWS.

•Applied advanced data structures and algorithms in the design and optimization of ETL processes, improving data processing efficiency on AWS.

•Ensured robust software engineering practices by integrating version control (Git/GitHub) and continuous integration tools (Jenkins) into the data pipeline development process, adhering to industry best practices.

•Utilized advanced SQL and data analysis skills to support business-critical applications and processes, with reports visualized in Tableau.

•Conducted performance tuning and optimization on AWS Redshift queries to enhance data retrieval efficiency.

•Implemented and maintained ETL processes using AWS Glue to manage data ingestion and transformation.

•Worked closely with data engineering teams to design and implement data lakes on AWS S3 for large-scale data storage.

•Managed the project workflow and task assignments using JIRA, ensuring timely delivery and accountability.

•Designed and deployed CI/CD pipelines using Jenkins and GitHub to automate deployment processes for analytics solutions.

•Participated in the development and deployment of analytics solutions on AWS QuickSight, enabling data-driven decision-making for clients.

Environment: AWS (Redshift, S3, Lambda, Glue, CloudWatch, QuickSight), Jenkins, Git, GitHub, SQL, Tableau, ETL Development, Data Governance, Zero Trust Security, Data Migration, Data Lakes, Data Quality Assurance, Agile, JIRA, CI/CD Pipelines, Performance Tuning, Python.

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