Mounika V Email and Phone Number

Designed and implemented scalable ETL pipelines using Python, SQL, and Apache Spark, reducing data processing times by 40%.Managed and optimized data warehouses on MySQL, PostgreSQL, and MongoDB, improving query performance by 30%.Developed and maintained data models and schemas, ensuring data integrity and consistency across systems, increasing data accuracy by 25%.Utilized Hadoop and Hive for big data processing and storage, enabling 50% more efficient data retrieval and reporting.Developed an internal testing tool framework using Python.Worked closely with business, transforming business requirements to technical requirements part of Design Reviews & Daily Project Scrums and Wrote custom MapReduce programs by writing Custom Input formats.Experience working with Google Cloud Platform to help strategize, architect, and implement various solutions to migrate data hosted on our on-prem platform to Google cloud Platform (GCP).Utilized data stored within Docker containers to create detailed metrics and logs on a Datadog dashboard, ensuring real-time monitoring and visibility of critical system performance indicators.Integrate geospatial data sources with GCP services such as BigQuery GIS, Google Maps Platform, and Google Earth Engine.Built RESTful APIs with Python to enable real-time data access and integration with third-party applications, enhancing data availability and accessibility.Experience in designing and implementing enterprise infrastructure and platforms required for setting up data engineering pipelines utilizing the tools available on the GCP Platform.Implemented robust error handling and logging mechanisms in Python scripts, ensuring reliability and traceability in data processing workflows.Optimized Python code and SQL queries to enhance the performance of ETL jobs, reducing processing times and resource consumption.Developed a mapping document to map columns from source to target.

• Conducted comprehensive manual testing of web and mobile applications, identifying and reporting bugs, which resulted in a 40 % reduction in production issues.• Developed detailed test plans, test cases, and test scripts based on functional specifications and requirements, improving test coverage by 30%.• Collaborated with development teams to understand application features and identify potential areas of concern, leading to early detection of 25% more defects.• Ensured the implementation and integration of Snowflake as a cloud-based data warehousing solution, enhancing data storage and analytics capabilities.• Parsed JSON documents with Python scripts for database loading.• Worked on Partitioning, Bucketing, Join optimizations and query optimizations in Hive.• Worked closely with business, transforming business requirements to technical requirements.• Ensured the scalability and performance of the monitoring solution by optimizing data ingestion and processing pipelines for both on-premises and cloud-based data sources.• Implemented automated data collection processes for both Docker and GCP environments, ensuring continuous and accurate data feed into Datadog for seamless metric tracking.• Designed and configured Datadog dashboards to aggregate and visualize metrics and logs from both Docker and GCP data sources, providing comprehensive monitoring solutions for stakeholders.• Successfully replicated the metrics and logging infrastructure by integrating data stored in Google Cloud Storage (GCP) into Datadog, expanding the monitoring capabilities to include cloud-based datasets.• Created Python merge jobs for data extraction and loading into MySQL• Ensured compliance with data security and privacy regulations while working with sensitive data in SQL Plus and SQL Navigator.• Implemented advanced Snowflake functionalities, such as data sharing and multi-cluster configurations, to improve the efficiency of complex analytical queries.

• Collaborated with development teams to understand application features and identify potential areas of concern, leading to early detection of 25% more defects.• Designed and implemented highly scalable and fault-tolerant data architectures on AWS, leveraging Amazon S3 for storage, Amazon Redshift for data warehousing, and AWS Glue for ETL processes.• Ensured the implementation and integration of Snowflake as a cloud-based data warehousing solution, enhancing data storage and analytics capabilities.• Ensured compliance with data security and privacy regulations while working with sensitive data in SQL Plus and SQL Navigator.• Implemented advanced Snowflake functionalities, such as data sharing and multi-cluster configurations, to improve the efficiency of complex analytical queries.• Monitored and optimized Amazon EMR cluster performance, tuning configurations and resource allocation for optimal efficiency• Implemented scalable and resilient storage solutions with Amazon S3, utilizing its object storage capabilities for efficient data storage and retrieval.• Implemented monitoring and alerting solutions for Apache Kafka clusters with Amazon MSK to ensure timely detection and resolution of issues• Engineered high-performance computing solutions on Amazon EC2, fine-tuning virtual machine configurations for optimal performance and scalability.

• Worked on analyzing data in the Hadoop cluster using big data tools including Pig, Hive, and Sqoop. Contributed towards maintaining Spark Applications using Scala and Python in conjunction with data development and software engineering teams. • Collected large amounts of log data using Apache Flume/Sqoop and aggregated using Pig/Hive in HDFS for further analysis. Worked on analyzing and writing Hadoop MapReduce jobs using Java API, Pig, and Hive. • Developed Spark SQL applications to perform complex data operations on structured and semi-structured data stored as Parquet. Used Pig to do data transformations, event joins filter bot traffic, and some pre-aggregations before storing the data onto HDFS. • Extract Transform and Load data from Sources Systems to Azure Data Storage services using a combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics. Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in In Azure Databricks.• Implemented real-time data processing pipelines with Python, Apache Kafka, and Apache Spark, allowing for immediate insights and decision-making.• Developed Python-based data validation scripts to monitor and ensure data quality throughout the ETL process, reducing errors and maintaining data integrity.• Created Pipelines in ADF using Linked Services/Datasets/Pipeline/ to Extract, Transform, and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool and backwards.• Developed Python scripts for interacting with relational (SQL) and NoSQL databases, enabling efficient data querying, indexing, and storage.

• Assisted in the development of data pipelines and ETL processes using Python and SQL, contributing to a 30% reduction in data processing errors.• Design, develop, and implement ETL (Extract, Transform, Load) processes using Informatica PowerCenter to extract data from various source systems, transform it according to business requirements, and load it into target data warehouses or databases.• Conducted data cleaning, wrangling, and transformation tasks, improving data readiness for analysis and reporting by 40%.• Contributed to the development of machine learning models using Scikit-Learn and TensorFlow for predictive analysis, increasing prediction accuracy by 15%.• Integrated data from multiple heterogeneous sources such as databases, flat files, XML, and web services into centralized data warehouses, ensuring data consistency, accuracy, and quality.• Optimized ETL processes for performance, including tuning Informatica mappings, sessions, and workflows to minimize load times and maximize efficiency.• Implement data quality checks and validation processes to ensure the integrity and accuracy of data being loaded into the target systems. Handle data cleansing, filtering, and transformation as needed.• Developed robust error handling mechanisms in ETL processes to capture and log errors, ensuring issues are identified and resolved efficiently.• Designed and managed Informatica workflows, sessions, and tasks, ensuring proper scheduling, execution, and monitoring of ETL jobs.• Participated in the design and implementation of data visualization solutions using Power BI, boosting user engagement by 20%.• Collaborated with data architects and business analysts to design and maintain data models, including creating and maintaining data dictionaries and metadata repositories.• Participated in data migration projects, moving data from legacy systems to new platforms, ensuring smooth transitions and minimal disruption to business operations.