1. Led SAP HANA and Oracle database migration to BigQuery, transforming Calculation views into GCP-compatible formats, and developing data pipelines with GCP's Airflow for ETL tasks.2. Proficiently utilized GCP services like DataFlow, Google Cloud Storage, and BigQuery, configuring services through Cloud Shell SDK for seamless integration.3. Collaborated with teams to develop frameworks for generating daily adhoc reports and transferring data from BigQuery to pandas or Spark data frames for advanced ETL capabilities.4. Engineered Spark ETL pipelines for processing large datasets and implemented streaming applications for real-time data processing, optimizing performance and leveraging Spark MLlib for machine learning.5. Managed Airflow clusters, ensuring optimal performance, resolving DAG issues promptly, and implementing best practices for DAG development and version control.6. Developed robust ETL pipelines using PySpark, collaborating with cross-functional teams to design optimized workflows, and creating custom PySpark UDFs and transformations to enhance data quality and integrity.

1. Led end-to-end data management initiatives, orchestrating ETL processes within Alteryx to extract and transform data from various sources including SQL Server, XML, Excel, and CSV files. Proficiently managed workflows and ensured seamless data migration and modeling tasks using Alteryx and SQL.2. Spearheaded the design and implementation of robust data pipelines on Google Cloud Platform, utilizing services like BigQuery, Cloud Dataproc, and Apache Airflow. Architected multiple pipelines for efficient data ingestion and transformation, fostering collaboration with cross-functional teams and facilitating knowledge transfer through comprehensive documentation.3. Demonstrated expertise in SQL development, employing advanced techniques such as joins, sub-queries, and nested queries to manipulate and refine data. Leveraged Alteryx workflows to handle scenarios with unavailable data, ensuring continuity and reliability in data processing.4. Integrated PySpark with other big data technologies like Apache Hadoop and Hive to create end-to-end data processing solutions, optimizing job performance and reducing processing time. Orchestrated and scheduled PySpark jobs using Apache Airflow, automating data processing workflows for enhanced efficiency.5. Collaborated with DevOps teams to containerize PySpark applications using Docker and Kubernetes, enabling easier deployment and scalability. Implemented data integration pipelines using Snowflake's native features and tools, such as Snowpipe and Snowflake Data Sharing, to ingest and share data with external partners and teams.6. Proficiently utilized reporting tools and software including SQL Database, Looker, Tableau dashboards, and data warehouses for insightful data visualization and analysis. Conducted rigorous SQL and LookML testing in Looker and Snowflake DB, while establishing custom table calculations and configuring data actions to enhance reporting capabilities.

1. Managed the migration of an Oracle database to BigQuery and implemented Power BI for reporting, while constructing Airflow data pipelines in GCP for ETL tasks.2. Demonstrated proficiency in GCP services including Dataproc, Google Cloud Storage, Cloud Functions, and BigQuery, with experience in data movement between GCP and Azure via Azure Data Factory.3. Designed Power BI reports on Azure Analysis Services for enhanced performance and utilized Cloud Shell SDK in GCP for configuring services and generating daily adhoc reports from BigQuery data.4. Collaborated with the Data Science team to implement advanced analytical models in Hadoop clusters and wrote optimized scripts in Hive SQL for creating complex tables.5. Extracted BigQuery data into Pandas or Spark data frames for advanced ETL capabilities and leveraged Google Data Catalog and Cloud APIs for monitoring and billing analysis.6. Conducted a POC for ML model utilization and Cloud ML for table quality analysis, possessed expertise in Cloud Dataflow, Apache Beam, and proficiently utilized Cloud Shell for various tasks and service deployments.