Amrutha Dintakurti

Azure Data Engineer with 2.5+ years of experience in developing scalable ETL pipelines using Azure Databricks, PySpark, Spark SQL, Delta Lake, and ADLS Gen2. Proficient in data transformation, validation, reconciliation, and Spark performance optimization for enterprise billing and reporting solutions.

Senior Software Engineer - Capgemini

(2023-12)

Project: Customer Billing and Invoice Processing Platform

• Developed and enhanced scalable ETL pipelines using PySpark on Azure Databricks to process customer billing data.
• Designed and maintained ETL workflows following the Medallion Architecture (Bronze, Silver, Gold) using Delta Lake to build validated, analytics-ready datasets.
• Optimized PySpark ETL jobs using partitioning, caching, broadcast joins, and Spark SQL optimization techniques to improve pipeline performance and reduce execution time.
• Processed customer billing data stored in Azure Data Lake Storage (ADLS Gen2) and transformed it into curated Delta Lake tables for downstream reporting.
• Read, cleansed, and transformed JSON and Parquet datasets using PySpark DataFrame APIs while applying business rules and schema validation.
• Applied business rules, data quality checks, validation, filtering, and transformations to ensure accurate and consistent data processing.
• Loaded transformed data into Delta Lake tables to support billing, reporting, and downstream analytical workloads.
• Developed SQL and Spark SQL queries to retrieve invoice, customer, and billing information from Oracle Database.
• Performed data reconciliation to identify missing records and data inconsistencies across multiple datasets.
• Investigated production issues and invoice discrepancies through root cause analysis and implemented fixes to ensure application stability.
• Collaborated with Business Analysts and QA teams during requirement analysis, testing, and production deployments.

Bachelor of Technology - Electronics & Communication Engineering - Shri Vishnu Engineering College for Women (2023)