Data Analyst Management
Resume:
Akhila Etikala
******.****@*****.***
Lead Data Analyst
PROFESSIONAL SUMMARY:
• Lead Data Analyst with over 6+ years of experience, specializing in diverse industry sectors, expert in PostgreSQL, NoSQL, MySQL for database management and data analysis.
• Advanced user of Microsoft Excel for data manipulation, analysis, and visualization, enhancing business reporting and insights.
• Skilled in Data Management Standards (DMS), Global Records Management (GRM), and Enterprise Data Management (EDM) frameworks, ensuring data quality, compliance, and lifecycle management in diverse data environments.
• Proficient in Data Quality and Control Practices, including Key Performance Indicators (KPIs) and internal controls for continuous improvement in data integrity and compliance.
• Skilled in QlikView for creating dynamic and interactive dashboards that facilitate data-driven decision-making processes.
• Utilized AWS CloudWatch for monitoring application performance and resource utilization, ensuring system reliability.
• Leveraged Grafana and Prometheus for visualizing and monitoring data analytics workflows, improving insights and operational performance.
• Experienced with AWS ECS and EKS for deploying and managing containerized applications, enhancing scalability and resource management.
• Experienced with Apache Hadoop for managing large datasets and performing distributed processing using the Hadoop framework.
• Utilizes Apache Spark for big data processing, enabling faster analytics and data processing capabilities in real- time scenarios.
• Proficient in Gitlab, GitHub and Bitbucket for version control, ensuring collaborative and error-free development environments in data projects.
• Expert in Informatica for ETL processes, enhancing data integration and workflow automation in data management tasks.
• Highly skilled in PowerBI, Tableau for developing comprehensive business intelligence solutions and interactive visualizations.
• Skilled in data warehouse technologies, including SQL and data modeling, to support robust data storage solutions.
• Experienced with SAP Business Objects and SSRS for reporting and data analytics, enhancing business intelligence capabilities.
• Proficient in MDX and DAX for advanced data querying within business intelligence applications and data warehouses.
• Skilled in data mining algorithms, applying statistical analysis to discover patterns and insights in large datasets.
• Utilizes MATLAB for advanced mathematical modeling and simulation, supporting complex data analysis tasks.
• Expert in Matplotlib and R for statistical visualization, enhancing the presentation and understanding of data insights.
• Experienced in Azure Stream Analytics for real-time data processing and analytics in cloud environments.
• Skilled in managing data within Azure SQL and Microsoft Azure, ensuring scalable and secure cloud data solutions.
• Proficient in AWS services like Lambda and S3 for scalable cloud storage and serverless computing functions.
• Experienced with AWS Redshift for data warehousing solutions, optimizing data storage and query performance.
• Utilizes Alteryx for data blending and advanced analytics, streamlining data preparation for analytical processes.
• Developed comprehensive data automation frameworks using Apache Spark ALS for predictive analysis and insights.
• Employed AWS Recognition for advanced image and video analysis projects, enhancing data enrichment and analysis.
TECHNICAL SKILLS:
Query Languages & Database
Management
PostgreSQL, NoSQL, MySQL, MS SQL server, Oracle
Data Analysis & Reporting Microsoft Excel, Power BI, SSRS, SAS, SAP Data Visualization QlikView, QlikSense, Tableau, Power BI, Looker Big Data Technologies Apache Hadoop, Apache Spark, Snowflake, Databricks Data Modeling Erwin, Microsoft Visio, Snowflake Schema, Azure Synapse Analytics, Toad Data Modeler, MongoDB, Cassandra
Programming/Scripting SQL/PL-SQL, R, Python (NumPy, pandas) Machine Learning TensorFlow, PyTorch, Amazon SageMaker, Azure ML Cloud Technologies GCP(Beam, Data Proc, Big Query, Data Flow, composer), Microsoft Azure(Blob storage, ADLS, ADF, Synapse, Azure SQL Server), AWS (Lambda, S3, Redshift, Athena, Dynamo DB, RDS, Glue)
Version Control Github, GitLab
ETL/ Data Integration Informatica, Alteryx, SSIS
Statistical Analysis R, MATLAB, DAX, MDX
Data Formatting CSV, Parquet, avro, json, xml, txt Data Migration Tools AWS DMS, Azure DMS, DB Convert, Apache Sqoop Data Warehousing Azure SQL, AWS Redshift, data warehouse management, Azure Data bricks Ticketing Tools JIRA, ServiceNow
Documentation & Reporting SharePoint, Confluence, Lucid Chart Agile and Scrum Methodologies Scrum, Agile
PROFESSIONAL EXPERIENCE
Client: United Health Group, Charlotte, NC Mar 2023 to till date Role: Senior Data Analyst
Roles & Responsibilities:
• Analyzed patient behavior and clinical data using MySQL and Python, providing insights that enhanced personalized care and treatment strategies.
• Collaborated with healthcare providers to forecast patient admission rates using R and Python, improving hospital resource planning and reducing patient care delays.
• Implemented Data Management Standards (DMS) and Global Records Management (GRM) Standards to maintain healthcare data integrity, ensuring compliance with Enterprise Data Management (EDM) policies
• Improved Data Quality and Control Practices by setting internal Key Performance Indicators (KPIs) and internal controls to monitor and enhance data accuracy and consistency.
• Collaborated with Data Management Stakeholders for gap identification and remediation, addressing out-of- tolerance conditions in patient data and devising action plans for continuous improvement.
• Performed adherence inspections for EDM/GRM policy and standards, ensuring compliance and alignment with regulatory requirements.
• Led QA assessments of data pipelines, developing controls to prevent metric breaches and optimizing data lifecycle management.
• Acted as a Subject Matter Expert (SME) in data monitoring and controls, advising on best practices for secure data capture, transport, and usage.
• Leveraged Enterprise Data Technology (EDT) for data transport and processing, aligning with audit and operational risk guidelines.
• Engaged in stakeholder engagement to align analytics outputs with healthcare goals, driving operational efficiencies and improved patient outcomes.
• Used tools like AWS QuickSight and Grafana to visualize KPIs and track compliance with data standards.
• Maintained advanced analytics dashboards using Tableau, providing critical insights into patient outcomes, operational efficiency, and healthcare trends.
• Integrated AWS S3 and Redshift for scalable, secure storage of electronic health records (EHR) and healthcare data, improving access and performance.
• Automated data workflows with Alteryx to accelerate healthcare data processing, reducing time-to-insight for patient care improvement.
• Deployed machine learning models using Amazon SageMaker to predict patient readmission risks and healthcare utilization trends.
• Utilized AWS Lambda to automate data processing tasks, improving workflow efficiency and reducing latency in real-time data analysis.
• Led the migration of healthcare data to AWS ECS and EKS for containerized applications, enhancing scalability and resource management.
• Streamlined healthcare operations using AWS EC2 instances for flexible and scalable computing power, optimizing data management and storage.
• Developed predictive models using R and Apache Spark on Databricks to forecast healthcare trends, enabling strategic decisions for patient care.
• Employed AWS CloudWatch for monitoring application performance and resource utilization, ensuring high availability of analytics dashboards.
• Integrated Grafana and Prometheus to visualize and monitor healthcare data analytics workflows, improving system reliability and performance insights.
• Used Excel and Tableau to translate raw clinical data into actionable insights, improving decision-making processes and patient outcomes by 25%.
• Led data migration projects to move patient records and healthcare data from legacy systems to AWS Redshift enhancing data availability and retrieval.
• Streamlined healthcare operations using AWS Cloud technologies, improving efficiency in data management and storage.
• Utilized JIRA for managing healthcare analytics projects, tracking progress, and coordinating work across interdisciplinary healthcare teams.
• Improved predictive healthcare analytics by building machine learning models using Python libraries such as PyTorch and TensorFlow.
• Created risk models with R and AWS QuickSight to assess financial risk in healthcare billing and insurance claims management.
• Documented healthcare data flows and processes, ensuring compliance with healthcare data privacy regulations such as HIPAA.
• Employed AWS Rekognition for advanced image-based healthcare data analysis, supporting radiology and diagnostics.
• Utilized Docker to containerize healthcare data analytics workflows, ensuring scalability and security in healthcare environments.
• Used GitHub for version control to manage scripts and healthcare data pipelines, ensuring secure collaboration and compliance with healthcare regulations.
Environment: MySQL, Tableau, EDM, DMS, GRM, KPIs, AWS S3, AWS Redshift, Alteryx, Amazon Sage Maker, Excel, R, Python, Apache Spark ALS, AWS Quick Sight,EC2, ECS, EKS, Cloudwatch, Grafana, Prometheus Tensorflow, PyTorch, AWS Rekognition, star schema, Docker, Git Hub, JIRA. Client: Walmart, Bentonville, AR Jun 2021 to Jan 2023 Role: Lead Business Data Analyst
Roles & Responsibilities:
• Managed PostgreSQL-based data analysis projects, ensuring high accuracy and relevance of analytics outputs in software services.
• Developed predictive models using Python libraries such as NumPy and pandas to enhance business trend forecasting.
• Developed Data Management Standards for financial data, ensuring compliance with company-wide EDM policies.
• Conducted audit and QA assessments on data models to ensure data quality and control practices aligned with internal KPIs and compliance requirements.
• Identified data gaps in logistics processes, implementing remediation strategies to address out-of-tolerance conditions and improve decision-making.
• Acted as a Subject Matter Expert (SME) in data lifecycle management, overseeing data capture, transport, and use according to EDM/GRM policies.
• Supported stakeholder engagement through actionable insights, optimizing processes and ensuring adherence to internal controls and operational risk guidelines.
• Developed adherence inspections to uphold data compliance and integrity across financial systems.
• Leveraged Snowflake’s cloud data warehouse to streamline data storage and retrieval processes, resulting in a 30% reduction in query execution time.
• Developed TensorFlow models to accurately forecast sales trends, enhancing inventory management strategies.
• Leveraged Databricks to streamline data processing and analysis, enabling faster execution of complex data transformations and machine learning task.
• Improved data processing speed by 25% by designing and implementing data models in MongoDB and Cassandra for effective unstructured data storage and querying.
• Utilized AWS services, including SageMaker for developing and deploying machine learning models, and Lambda for serverless data processing tasks.
• Deployed AWS EC2, ECS, and EKS for scalable compute resources, ensuring efficient handling of data workloads during peak demand.
• Leveraged AWS CloudFormation to automate the provisioning and management of AWS infrastructure, ensuring consistency and compliance in cloud resources.
• Utilized Apache Sqoop to efficiently migrate data between PostgreSQL databases and Hadoop, streamlining the ETL process.
• Analyzed customer behavior using PyTorch, tailoring marketing efforts based on advanced machine learning insights.
• Created dynamic performance dashboards in Power BI, providing real-time insights to retail managers and decision makers.
• Collaborated with cross-functional teams in requirements gathering and data modeling phases of the SDLC to develop supply chain analytics solutions.
• Designed data pipelines in AWS and Databricks to ingest, process, and analyze large datasets, enhancing supply chain optimization.
• Leveraged monitoring tools such as AWS CloudWatch, Grafana, and Prometheus to track system performance, ensuring high availability and reliability of data pipelines.
• Used SDLC methodology to lead the development of machine learning models and implement predictive analytics for logistics data.
• Utilized Snowflake’s scalable architecture to handle large volumes of data, enabling efficient analysis and reporting for high-traffic periods.
• Worked within Agile and Scrum methodologies, ensuring timely delivery of analytics projects in a dynamic environment.
• Implemented machine learning algorithms using MATLAB, addressing complex data challenges in the software services sector.
• Utilized Jenkins to automate the deployment of data pipelines and ensure continuous integration/continuous delivery (CI/CD) processes, reducing manual efforts and accelerating deployment times.
• Maintained repository integrity by performing code reviews and managing feature branches in GitLab, ensuring consistent data pipeline updates and model versions.
• Managed tasks and tracked project progress using Rally, ensuring efficient backlog grooming, sprint planning, and timely issue resolution
• Streamlined ETL processes using Apache NiFi, ensuring accurate and timely data availability for analysis. Environment: PostgreSQL, AWS Lambda, EDM, GRM, KPIs, cloudwatch, Databricks, Sagemaker, EC2, ECS, EKS, Python
(NumPy, pandas, TensorFlow, PyTorch), PowerBI, Mongo DB, Cassandra, Snowflake, MATLAB, Jenkins, Jupyter Notebook, Excel, PySpark, Confluence, Apache NiFi, Apache Databricks, Scrum, Agile, Gitlab, Rally, Apache Sqoop. Client: Synechron INC, Hyderabad, India Apr 2019 to Mar 2021 Role: Data Analyst
Roles & Responsibilities:
• Managed data integration across platforms using SSIS, enhancing system connectivity and improving data accessibility for financial reporting.
• Developed XML and JSON schemas for structured financial data exchange, standardizing data formats across financial applications.
• Facilitated secure data transfers (FTP/SFTP), maintaining high standards of security and compliance for sensitive financial data.
• Established Data Quality and Control Practices within financial data management systems, improving data accuracy and regulatory compliance.
• Defined and monitored Key Performance Indicators (KPIs) for financial reporting, ensuring alignment with internal controls.
• Conducted compliance audits and QA assessments, adhering to Data Management Standards and Global Records Management (GRM) Standards for financial data handling.
• Created action plans for gap identification and remediation to address metric breaches and enhance data accuracy.
• Utilized EDM/GRM policy and standards for secure data exchange, ensuring continuous monitoring and compliance with audit requirements.
• Designed and maintained RESTful APIs to enable real-time financial data integration and system interoperability.
• Utilized Bitbucket for version control, ensuring integrity of financial data transformations and processes.
• Managed the full SDLC for data integration processes, including requirements gathering, design, and testing of ETL pipelines using Informatica and Oracle Data Integrator.
• Led a BI team in integrating real-time financial data collection, resulting in a 20% increase in financial data accuracy.
• Analyzed financial data using MS SQL Server, generating key insights that enhanced financial forecasting, budgeting, and decision-making processes.
• Employed Kafka for real-time financial data streaming, improving data availability for time-sensitive financial operations.
• Designed business intelligence dashboards to improve financial data accessibility, reducing query time by 20% for financial teams.
• Implemented data quality checks on financial datasets using Informatica, ensuring accuracy and reliability in financial reporting.
• Incorporated Power BI and Qlik Sense to create interactive financial dashboards, increasing stakeholder engagement.
• Automated monthly financial reporting pipelines, saving up to 15% man-hours per week by streamlining data processing.
• Developed predictive financial models using MATLAB and R to support financial forecasting and risk management strategies.
• Maintained financial data warehouses using Azure SQL, ensuring scalability and robust management of large financial datasets.
• Utilized Azure Data Factory (ADF) to move financial data from various sources to destinations like Azure Synapse and Azure Data Lake for analysis.
• Applied advanced Excel features for in-depth financial data analysis, including pivot tables, VLOOKUP, and scenario analysis, improving efficiency in day-to-day financial operations.
• Developed data models using Microsoft Visio, creating clear entity-relationship diagrams (ERDs) and process flows for streamlined data architecture in financial systems.
• Leveraged Azure Synapse Analytics for large-scale data processing and real-time analytics, facilitating data modeling and reporting for financial operations.
• Applied Terraform for infrastructure automation, enhancing efficiency in deploying cloud resources for financial data platforms and ensuring consistency in environments.
• Utilized Ansible for configuration management, automating the deployment of financial data pipelines and supporting seamless integration across cloud environments.
• Migrated financial data using Azure Database Migration Service (DMS), ensuring minimal downtime and data integrity during transitions from on-premise to cloud systems.
• Employed DBConvert to enable smooth migration of financial data across various databases, ensuring cross- platform compatibility and performance optimization. Environment: SSIS, Informatica, EDM, GRM, KPIs, XML, JSON, FTP/SFTP, RESTful APIs, Bitbucket, MS SQL, Apache NiFi, Kafka, Microsoft Excel, Power BI, Qlik Sense, MATLAB, R, Azure Stream Analytics, Azure SQL, Microsoft Visio, Azure synapse analytics, Terraform, Ansible, DB convert and Excel. Client: Sovereign Software Solutions, Pune, India Mar 2018 to Feb 2019 Role: Data Analyst
Roles& Responsibilities:
• Developed queries using NoSQL and scripts to extract and analyze data, supporting business intelligence initiatives.
• Carried out cleansing and transformation of raw data using Python and Scala, increasing data accuracy by 20%.
• Managed Apache Hadoop clusters for big data processing tasks, enhancing data analysis capabilities.
• Designed and executed ETL workflows using SSIS and Abnitio, improving data integration and consistency.
• Implemented data visualizations in Looker, providing actionable insights through dynamic reporting.
• Employed GITHub for source code management, facilitating effective version control and collaborative development.
• Leveraged Apache Spark for advanced data processing tasks, enhancing analytics and data handling speed.
• Automated routine data tasks using Informatica, reducing manual efforts and improving efficiency.
• Streamlined data workflows using Apache Hadoop, ensuring efficient handling of large-scale data sets.
• Conducted statistical analysis using DAX and MDX, supporting complex financial assessments and business analytics.
• Utilized SAP Business Objects for creating advanced reporting solutions, optimizing data visualization and user interaction.
• Developed and maintained SSRS reports, providing reliable and timely business intelligence to support decision- making.
• Created logical and physical data models using Erwin and Toad Data Modeler, improving database schema designs and data governance.
• Leveraged Talend and AWS DMS for migrating data between legacy systems and modern cloud platforms, ensuring minimal downtime and data integrity during transitions.
• Used ServiceNow and Jira for issue tracking and workflow management, ensuring timely resolution of data issues and smooth collaboration across teams.
• Enhanced business reporting and analytics through the development of interactive dashboards in Tableau. Environment: Python, Scala, NoSQL, QlikView, Apache Hadoop, SSIS, Abnitio, Tableau, Looker, Microsoft Excel, Apache Spark, DAX, MDX, SAP Business Objects, SSRS, GitHub, Erwin, Toad data modeler, Talend, AWS DMS, ServiceNow, JIRA. EDUCATION:
BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE 2014 - 2018 Jawaharlal Nehru Technological University Hyderabad (JNTUH) - INDIA
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