PALLAVI BHARDWAJ

Company Overview: Client - Aetna

• Spearheaded a large-scale GCP migration project, successfully transitioning the company's data infrastructure to the cloud
• Worked on migrating GCP tenants from shared to their own compute projects in order to monitor expenses, utilization, and improve the estimation of their business budget.
• Refactored existing on-premises code to Airflow DAGs, leveraging BigQuery and Python for efficient data processing
• Developed and optimized SQL queries to extract, transform, and load data from various sources
• Conducted numerous POCs involving FTP, Dataproc, and other technologies to evaluate their suitability for specific use cases.
• Led the data migration effort, ensuring data integrity, security, and compliance throughout the process
• Collaborated with cross-functional teams to ensure accurate data migration and to resolve other dependencies for smooth migration process
• Automated the process to validate and compare tables migrated by two different tools for data migration team
• Automated the data ingestion process using Dataproc, ingesting data from BigQuery to various databases
• Provided training and mentorship to team members, fostering knowledge sharing and professional growth
• Participated in agile development processes, contributing to sprint planning, stand-ups, and reviews to ensure timely delivery of data projects
• Optimized SQL queries and database schemas for performance improvements in data retrieval operations
• Designed, constructed, and maintained scalable data pipelines for data ingestion, cleaning, and processing using Python and SQL
• Implemented data visualization tools like Tableau and Power BI to create dashboards and reports for business stakeholders
• Developed Python scripts for extracting data from web services API's and loading into databases
• Managed version control and deployment of data applications using Git, Docker, and Jenkins
• Analyzed user requirements, designed and developed ETL processes to load enterprise data into the Data Warehouse
• Automated ETL processes across billions of rows of data, which reduced manual workload by 29% monthly
• Designing and developing scalable data pipelines using Google Cloud Platform (GCP) services such as Cloud Dataflow, Cloud Composer, and Apache Beam to process and manage large financial datasets efficiently.
• Building and optimizing data storage solutions leveraging Big Query, Cloud SQL, and Cloud Spanner to ensure high-performance data retrieval and analytics for financial reporting and compliance needs.
• Developing ETL/ELT workflows using Cloud Data Fusion, Cloud Storage, and Python, ensuring seamless data ingestion from various sources, including transactional banking systems and third-party financial services.
• I worked with data scientists to design data structures and support machine learning (ML) models using Google AI Platform and Big Query ML.
• Implementing CI/CD pipelines for data workflows using Cloud Build, Terraform, and GitHub Actions, automating deployment, testing, and monitoring processes to ensure consistency and reliability.
• Developing and maintaining data pipelines using GCP services such as Cloud Dataflow, Big Query, and Cloud Pub/Sub to ensure seamless data ingestion, transformation, and storage.
• Automating infrastructure provisioning and deployments using Terraform, Cloud Deployment Manager, and CI/CD pipelines with Cloud Build and GitHub Actions to ensure consistency and scalability.
• Supporting machine learning and AI initiatives by preparing and transforming large datasets for model training and evaluation using Big Query, Vertex AI, and Cloud AI Platform.
• Monitoring and troubleshooting data workflows using Cloud Logging, Cloud Monitoring, and Error Reporting, ensuring minimal downtime and quick resolution of data pipeline issues.
• Migrating on-premises Hadoop clusters (including HDFS, YARN, and MapReduce) to Google Cloud Storag

Environment: Google Cloud Platform (GCP), Apache Beam, BigQuery, Cloud SQL, Cloud Spanner, ETL/ELT workflows, Cloud Data Fusion, Apache Kafka, Google AI Platform, CI/CD pipelines, and GitHub Actions.

Company Overview: Client - British Airways

• Monitored brand mentions and reviews across various platforms, performing sentiment analysis using Python libraries (Beautiful Soup, TextBlob) for brand monitoring and reputation management
• Created interactive Tableau dashboards to showcase sentiment analysis, identifying friction points and improving customer perception, resulting in a ~10% increase in conversion rate
• Analyzed 3 million records of airline data using SQL to create reporting dashboards in Power BI, highlighting KPIs from various operational aspects for stakeholders
• Worked on a pilot project to develop fraud triggers using historical data to flag high-risk transactions and identify drivers of fraud bookings
• Led a team of 5 data analysts, compiling daily, weekly, monthly, and annual reports, and conducting ad hoc data mining for executive leadership
• Analyzed complex datasets to identify trends and patterns

Environment: Tableau Desktop, Tableau Server, Power BI Desktop, Power BI Service, SQL Server, Python, Excel

Environment: Tableau Desktop, Python, PHP, AngularJS, Jira, SQL, Excel.

Company Overview: Client - Imarticus Learning, Jubi, DGC

• Developed and implemented comprehensive solutions for multiple Customer Relationship Management (CRM) campaigns, facilitating seamless outreach to customers across various channels, including email, web, and mobile, tailored to meet client-specific requirements
• Presented findings and insights through visualizations and written reports to stakeholders
• Developed a full-stack online learning platform using PHP, MySQL, and Angular, featuring secure user authentication, course management, interactive learning modules, and gamification elements
• Created an interactive data visualization dashboard using Tableau and Python to analyze student performance data, identify learning gaps, and provide actionable insights to educators and administrators
• Designed and developed a learning platform with two distinct dashboards: one for the instructor and another for the students, featuring real-time data updates and interactive visualizations.
• Automated the process of fetching student results every 30 minutes and updating the database and instructor's dashboard, ensuring timely access to up-to-date information

• Embedded System Design Based Projects and Electronics Hardware Based Projects: Hardware & Firmware Designing, Project Monitoring & Testing
• Developed protocols like CAN, Modbus, and USB for microcontrollers used for communication
• Embedded System Design Based Projects and Electronics Hardware Based Projects: Hardware & Firmware Designing, Project Monitoring & Testing