Supriya Chandru

• Architected end-to-end ELT pipeline to migrate on-premise sales data into ADLS Gen2 using Azure Data Factory and Azure Databricks, designing a scalable Lakehouse architecture based on the Medallion (Bronze, Silver, Gold) framework.
• Designed initial full load ingestion framework to load historical sales data into the Bronze layer, followed by monthly incremental loads using Auto Loader with schema evolution and checkpointing for reliable change data processing.
• Built parameterized ADF pipelines to orchestrate Databricks Workflows and notebooks, enabling metadata-driven ingestion, dependency handling, incremental processing, and automated failure recovery.
• Implemented optimized Delta Lake transformations with ACID compliance, partitioning, and performance tuning strategies to support high-volume monthly sales reporting and downstream analytics.
• Established enterprise governance, security, and DevOps practices using Unity Catalog, RBAC, Managed Identities, Azure Key Vault, Azure DevOps CI/CD pipelines, and monitoring through Azure Monitor and Databricks job alerts.
  •Designed and deployed a customer churn prediction model by implementing a complete MLOps lifecycle on Databricks, integrating MLflow for model tracking, versioning, and registry; utilized GitHub Actions to automate model training, validation, testing, evaluation, and seamless promotion across Dev, Staging, and Production environments.
  

•Developed and implemented a Linear Regression model to predict Airbnb customer pricing, performing data preprocessing (data cleaning, handling missing values, and normalization) and feature engineering (encoding categorical variables and deriving key features), followed by model training and evaluation; leveraged Databricks and MLflow for experiment tracking and model management, and implemented CI/CD pipelines using GitHub Actions for automated testing, evaluation, and deployment across Dev, Staging, and Production environments.
•Conducted data preprocessing, feature engineering, and model
evaluation to optimize model performance and accuracy
•Collaborated with cross-functional teams to understand business
requirements and translate them into actionable insights and
recommendations
•Designed, developed, and implemented technical data and analytic
solutions for HSBC Wholesale Banking Group using Azure cloud services
•Integration of solutions for continuous deployment
•Worked on the creation of scalable CI/CD pipelines

Architected a robust ELT data pipeline using Azure Data Factory and Azure Data Lake Storage, implementing a medallion-style architecture (Landing, Curated, Processed layers) designed and built dimensional data models (Fact and Dimension tables) for sales and orders, optimized with full and incremental loading strategies, and delivered analytics-ready data into Azure SQL Warehouse.

Collaborated with cross-functional teams to define data modeling
standards and best practices, ensuring alignment with business
objectives and scalability of data solutions architecture
Developed Databricks notebooks for Dim tables creation using PySpark, SparkSQL for Fact tables
Implemented validation notebooks for data validation in different layers
AWS Glue for ETL pipeline creation and data import from PostgreSQL
Proposed, designed, and implemented data pipelines for ETL on AWS and
Azure

• Proficient in analyzing business requirements and specifications to develop and execute comprehensive test cases.
• Conducted testing for standalone and web application features, ensuring full test coverage.
• Extensive experience in developing test plans, creating test cases, and logging defects.
• Skilled in performing GUI, Functional, Integration, System, Regression, E2E, and Database testing.
• Proficient in regression testing, error documentation, and troubleshooting.
• Monitored project progress and reported status against key project milestones.
• Coordinated with business users to facilitate User Acceptance Testing (UAT).
• Reviewed and validated test scenarios and test cases to ensure accuracy and completeness.
• Assessed and maintained the Requirement Traceability Matrix to ensure alignment with business requirements.
• Provided regular test status updates and detailed reports to senior management.

•Analyzing big datasets, developed Spark API's, and converted Hive/SQLqueries into Spark transformations
•Imported tables from RDBMS to HDFS using Sqoop and utilized Kafka for real-time streaming
•Designed and proposed data pipelines for data ingestion into Hadoop &Data Lake

• Migrated on-premise applications to AWS cloud
  •Managed migration of almost 500 servers, including 8 business units
  •Created VPC for complete control over the virtual networking environment
  •Worked on CloudFormation scripts for creating VPC, subnets, route tables, and NACLS
  •Implemented AWS Glue for ETL pipelines, importing data from
  PostgreSQL using Crawler