Bipina Poudel

Engineered scalable ETL workflows to process large volumes of marketing and operational data, supporting daily campaign execution in enterprise data warehouse and ODS environments.

• ▸Automated ingestion of structured and semi-structured data (JSON, flat files, APIs, FTP/SFTP) to eliminate manual file management
• ▸Built SQL-based transformation workflows to produce campaign-ready audience and segmentation data
• ▸Optimized warehouse queries using indexing, partitioning, and dimensional modeling to improve reporting responsiveness
• ▸Developed reusable, curated tables and views to standardize business logic for marketing analytics teams
• ▸Structured logging and reconciliation processes to eliminate recurring production pipeline failures
• ▸Maintained Hive and relational warehouse tables to support daily campaign execution and operational reporting

Environment: AWS, S3, Glue, Spark, Kafka, Snowflake/Redshift, Python, SQL, Airflow, Jenkins

Designed and built scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, and Delta Lake to process high-volume healthcare datasets in a HIPAA-compliant environment.

• ▸Implemented medallion architecture (bronze, silver, gold layers) for claim, pricing, and membership policies
• ▸Applied Delta Lake features including ACID transactions, schema evolution, CDC, and optimized file compaction
• ▸Enforced PHI-compliant data architecture using Unity Catalog, access controls, and governance guardrails
• ▸Optimized Spark jobs with partition strategies, broadcast joins, caching, and Z-order indexing
• ▸Developed slowly changing dimensions to maintain historical claims and member tracking
• ▸Supported cost-efficient lakehouse operations using cluster sizing optimization and workload management

Environment: Azure Data Factory, ADLS Gen2, Databricks, Spark, PySpark, SQL, Snowflake, Airflow, Power BI

Implemented ETL processes and dimensional data models for financial reporting, investment analysis, and regulatory compliance in the fintech domain.

• ▸Developed dimensional data models (star and snowflake schema) for financial reporting and investment analysis
• ▸Constructed and optimized data pipelines utilizing Snowflake and AWS Redshift
• ▸Authored complex SQL code for transformations, views, and stored procedures for regulatory and risk reporting
• ▸Supported migration of on-premise data pipelines to AWS S3 and Redshift
• ▸Implemented role-based access controls and encryption standards to ensure data security (SOX compliance)
• ▸Conducted data reconciliation and validation to ensure accuracy in financial data

Environment: Informatica, AWS S3, Redshift, Oracle, SQL Server, SQL, Python, Tableau