Manikanth Shankari

• Migrated critical datasets from an Oracle database to Snowflake, utilizing Azure Data Factory (ADF) for efficient ETL
  processes, data scalability, query performance and data orchestration
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  Engineered data pipelines in Azure Databricks, leveraging Python and SQL to replicate and enhance Snowflake-based reporting
  logic in the cloud, ensuring seamless integration with Power BI and smooth transition to a cloud-based data warehousing
  solution
  • Worked with Hive on top of Hadoop for executing SQL queries and manage large datasets residing in distributed storage
  •Developed Data Ingestion tool (Data Lake as a Service) that handles full and incremental load using Talend, Tool ingests diverse
  data objects (Oracle, MongoDB, PostgreSQL, CSV, XML files) into Data Lake built on Azure Data Lake Gen 2

• Python programming to conduct data analysis on preventive maintenance records for 1000+ equipment units. Employed
regression analysis techniques with Pandas and NumPy to forecast maintenance costs at the outset of each fiscal year.
• Utilized Kafka for real-time processing of preventive maintenance logs on equipment data, managing over 300 messages per minute, and
stored the processed data in AWS S3. Additionally, employed AWS Glue crawlers for automated metadata extraction.
• Streamlined ELT tasks using PySpark, enhancing data processing efficiency by 20% and integrated with Redshift data warehouse for
optimized storage and analysis.

• Performed data analysis using PowerBI techniques to create BI dashboards and reports, identifying key trends, patterns and
communicating insights from marketing campaigns.
• Compiled monthly performance reports, emphasizing key performance indicators (KPIs) using advanced Excel features, including VBA
and VLOOKUP functions.
• Conducted predictive modeling using SPSS, and SAS to forecast sales trends, achieving a 25% accuracy rate and informing strategic
business decisions.
• Mitigated fraud transactions by 17% in offline retail environments through the implementation of real-time, data-driven rules using SQL,
Association rule mining, K-means clustering and predictive modeling.
• Drove a 20% increase in CRM growth using Salesforce by deploying advanced models incorporating RFM, Demand Forecasting, and Market Basket Analysis for customer retention, and cross-sell product analysis through real time campaigns

•Extracted, transformed, and loaded over 1TB data from SQL Server, HTTP end points and REST APIs into Hive external tables
via Sqoop and Spark ETL processes
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Integrated retail and corporate sales data with PySpark, utilizing UDFs for aggregation and Python scripts for Data Frame
manipulation, resulting in a 30% reduction in processing time and handling over 2 million records daily
•
Orchestrated end-to-end data workflows and pipelines by leveraging Azure Data Factory, enhancing data integration and
management
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Designed and managed scalable data warehouse solutions in Azure Synapse Analytics, handling over 5 TB of data and
improving query performance by 10% for high-performance analytics and reporting
•
• Created Power BI reports and dashboards utilizing DAX expressions and complex data models to provide sales insights