Anusha Pranandi Email & Phone Number

Pittsburgh, Pennsylvania, United States

 Collaborated with cross-functional teams to gather data requirements and design data models for new projects. Implemented data quality and governance practices within data engineering projects. Managed ETL (Extract, Transform, Load) tasks ensuring data integrity and pipeline stability. Developed and maintained end-to-end operations of ETL data pipelines with Azure Data Factory (ADF). Designed advanced analytics models, including descriptive, predictive, and machine learning techniques. Converted Pig scripts/components of the ETL process to Spark API for improved performance. Utilized GCP Cloud-focused solutions to architect and implement data pipelines, leveraging services such as BigQuery for efficient data storage and analysis. Implemented optimized Spark jobs to process and analyze large volumes of data. Deployed data factory for creating data pipelines to orchestrate the data into SQL databases. Employed GCP Cloud-focused tools to create scalable and cost-effective data architectures, ensuring optimal performance and resource utilization. Attend Google Cloud Platform (GCP) webinars, conferences, and community events to understand the emerging best practices, updates, and use cases for GCP BigQuery and other associated GCP data services. Executed test plans for connectivity, SIT (System Integration Testing), and UAT (User Acceptance Testing), applying 8 years of data engineering experience to ensure high-quality data solutions. Implemented data security and compliance measures in GCP Cloud-focused environments, ensuring data protection and adherence to regulatory requirements. Developed automated CI/CD and test-driven development pipelines using Azure DevOps. Delivered insights using Azure Power BI, Azure SQL Database, and Data Warehouse. Developed and implemented data quality automation tests using Python and PySpark.

Madison, Wisconsin, United States

 Led the full project lifecycle from requirements gathering to system integration. Utilized Agile Scrum methodology for project management. Designed and implemented cloud solutions using Microsoft Azure. Integrated GCP Cloud-focused services with other cloud platforms to create seamless and interoperable data solutions, enhancing data flow and accessibility. Ensured compliance with enterprise data standards, governance, and quality. Developed big data solutions for pattern matching and predictive modeling. Developed robust ETL processes to extract, transform, and load data from various sources, ensuring data consistency and integrity Developed ETL pipelines in Azure cloud for customer data processing. Used Cosmos DB for event sourcing in order processing pipelines. Automated data generation scripts with Python and Azure Databricks. Analyzed and implemented SQL scripts using PySpark for enhanced data processing. Utilized 8 years of data engineering experience to develop and implement test metrics and reporting frameworks, providing clear insights into testing progress and outcomes Regularly review GCP BigQuery documentation, release notes, and community blogs to stay informed on optimizations, new SQL functions, performance improvements, and integrations with other GCP tools. Utilized GCP Cloud-focused solutions to architect and implement data pipelines, leveraging Big Query for efficient data storage and analysis. Developed Oozie workflows for ETL cycles and integrated with Jenkins for continuous integration. Used Delta Lake for reliable data storage in data lakes. Implemented data profiling using Python libraries such as pandas, numpy, and scipy. Employed GCP Cloud-focused tools to create scalable and cost-effective data architectures, ensuring optimal performance and resource utilization. Implemented Data Quality Validations on Data Lake and Data Warehouse.

Ny, New York, United States

 Leveraged BigQuery in GCP Cloud-focused projects to perform complex data transformations and aggregations, enabling advanced analytics and insights Followed SDLC methodologies throughout the project lifecycle. Implemented data pipelines as per data models. Processed large datasets using Hadoop and Big Data technologies. Utilized Snowflake for big data solutions and implemented SnowSQL and SnowPipe. Created ETL processes in Azure Data Lake, Data Factory, and Databricks. Developed Spark scripts using Scala and Python for efficient data processing. Designed and developed automated testing frameworks using GCP components such as DAG/Airflow, Dataflow, PySpark, PubSub, Python, and gsutil commands, ensuring comprehensive test coverage for data pipelines. Consistently explore and stay up-to-date with the latest advancements in GCP BigQuery by reviewing new features, updates, and improvements introduced in BigQuery and its related services. Implemented data security and compliance measures in GCP Cloud-focused environments, ensuring data protection and adherence to regulatory requirements Created and maintained multi-node Kafka clusters for data streaming. Designed and implemented data governance and data quality processes. Automated data validation and quality checks using Great Expectations and other libraries. Developed monitoring and alerting solutions using Grafana and Prometheus. Integrated data from various sources using APIs. Implemented data consumption via APIs for real-time data analysis.

Hyderabad, Telangana, India

 Analyzed business requirements and designed business solutions. Developed ETL pipelines using Python and Redshift. Implemented data cleansing, security, profiling, and monitoring processes. Designed and developed HBase schemas for large datasets. Integrated GCP Cloud-focused services with other cloud platforms to create seamless and interoperable data solutions, enhancing data flow and accessibility Developed SSIS packages for data extraction and transformation. Implemented data quality and validation processes for Data Lake and Data Warehouse. Experiment with new features in BigQuery such as machine learning (BigQuery ML), streaming data ingestion, materialized views, and BigQuery Omni to keep technical skills relevant and aligned with industry demands. Developed automated data profiling and analysis scripts using ydata-profiling and DataProfiler. Collaborated with data scientists to optimize data processing and analysis workflows. Implemented CI/CD pipelines using Git and Jenkins. Utilized NoSQL databases like MongoDB for scalable data storage solutions. Conducted data analysis and visualization using Tableau and Power BI.

Hyderabad, Telangana, India

 Created DDL/DML scripts for data models and interacted with stakeholders for requirements gathering. Developed data models for Enterprise Data Warehouse (EDW). Captured and published metadata in accordance with data governance policies. Used Erwin for conceptual, logical, and physical data modeling. Developed reports using SQL Server Reporting Services (SSRS) and performed data analysis with PL/SQL. Worked at conceptual/logical/physical data model level using Erwin according to requirements. Managed end-to-end data ingestion, processing, and analysis pipelines using Hadoop ecosystem tools. Engage with BigQuery user groups and forums, leveraging community insights to stay aware of the latest advancements in BigQuery and troubleshoot any challenges encountered with real-world implementations. Utilized Python and SQL for data analysis and reporting, generating insights that supported strategic business decisions. Collaborated with cross-functional teams to optimize data storage and querying efficiency. Utilized Azure cloud services for data management. Implemented logging, monitoring, and notification solutions using New Relic and Prometheus. Developed data quality and profiling solutions using data science libraries. Gained domain knowledge in the telecom sector to enhance data processing and analysis strategies.