User details
PP
PP
Prashanthi Ponakalla
@prashanthi_ponakalla_z9dxzpyqk
Data Engineer
Halethorpe, MD, United States
0Follower0Following0Mutual0Post
Member since: Jan 21, 2025Experienced Data Engineer with 4+ years of expertise in designing and implementing data solutions using cloud platforms and modern data tools. Skilled in leveraging AWS (S3, Redshift, Glue) and Azure (Data Factory, Synapse Analytics, Data Lake) for data integration, warehousing, and analytics. Proficient in developing ETL pipelines using PySpark, Databricks, Python, SQL, and AWS Glue. Adept at creating impactful visualizations using Power BI and Tableau. Experienced in optimizing data pipelines and workflows to improve performance and scalability, ensuring seamless data processing for critical business operations. Strong communication and collaboration skills, fostering effective teamwork and stakeholder engagement.
• Designed and implemented scalable data pipelines for enterprise operations using AWS services (S3, Lambda, Glue, Redshift), increasing data processing speed by 35% and improving inventory tracking efficiency.
• Developed ETL workflows on Databricks to ingest, clean, and transform large datasets, utilizing Apache Spark's capabilities to ensure data accuracy and streamline processes, resulting in a 40% reduction in data preparation time.
• Leveraged Apache Spark on AWS EMR for big data processing, enhancing data throughput by 50% and improving analytics performance by 30%, enabling real-time insights for critical business operations. . Built data ingestion pipelines with PySpark to process structured and semi-structured data (CSV, JSON, Parquet), streamlining data integration workflows.
• Optimized data processing and reporting workflows within AWS Redshift, utilizing materialized views and compression techniques, improving query performance by 25%.
• Managed SQL databases for improved data storage and retrieval, ensuring efficient handling of diverse data storage requirements.
• Developed advanced SQL queries to extract, analyze, and manipulate data, enabling efficient reporting and actionable insights.
• Designed interactive dashboards and reports in Power BI, delivering key performance metrics and business insights, leading to a 10% improvement in decision-making speed.
• streamlined data pipeline performance by implementing partitioning, indexing, and parallel processing techniques, reducing execution time by 30% and ensuring high availability for large-scale data processing tasks.
• Enforced data storage solutions using Amazon Redshift, reducing data retrieval times by 20% and enhancing fraud detection algorithm performance.
• Developed and maintained ETL workflows using Apache Airflow, improving workflow reliability by 30% for transaction processing systems.
• Established and enhanced data models to support business intelligence and analytics, increasing report generation efficiency.
• Performed data cleaning, validation, and integration to ensure data quality and consistency, achieving 99% data accuracy in customer transaction records. . Led the migration of on-premises data systems to AWS cloud environments, reducing infrastructure costs by 15% and improving scalability for global operations. . Built and deployed SSIS packages for ETL processes and utilized SSAS for analytical processing, improving data processing speed by 20% for compliance reporting. . Presented technical documentation and reports for both technical and non-technical stakeholders improving decision-making processes.
• Integrated Python with big data frameworks (PySpark, pandas) for efficient large-scale data processing, and developed automated data validation scripts to maintain data integrity.
• Designed and developed interactive dashboards and reports in Power BI, enabling real- time monitoring of key performance indicators (KPIs) and driving data-driven decision- making across business units.
• Automated routine data engineering tasks, including job scheduling and monitoring, using Python and Airflow, reducing manual intervention by 40% and improving operational efficiency
• Executed a data warehousing solution using Azure Synapse Analytics and Data Factory, reducing data storage costs by 20% and optimizing IT infrastructure management. . Built ETL pipelines with Azure Data Factory to integrate data from diverse on-premises and cloud-based sources, reducing integration time by 30% and enhancing cloud services efficiency.
• Managed and optimized data lakes using Azure Data Lake Storage Gen2 to store and process structured and unstructured data, improving storage efficiency by 25% for advanced analytics.
• Developed ETL workflows with Azure Databricks, leveraging Apache Spark and Azure's infrastructure for large-scale data processing, increasing processing speed significantly. . Enhanced data processing speeds and reporting accuracy with Azure SQL Database, reducing report generation times for sales and customer support teams. . Utilized Azure Data Factory to orchestrate data movement and transformation, improving pipeline efficiency for software development lifecycle da ta.
• Developed and boosted complex SQL queries, stored procedures, and views for data extraction, analysis, and performance tuning from relational databases.
• Created interactive dashboards and reports in Tableau, improving data visibility and supporting informed decision-making for product development teams.