Sujith G Email & Phone Number

Seattle, Washington, United States

• Contributed to the migration of financial data from SAP ECC system to a to a Snowflake-based architecture, ensuring reliable and ACID-compliant data storage by leveraging Snowflake’s unique capabilities such as time travel and zero-copy cloning. • Assisted in building data pipelines using Fivetran for automated and seamless data ingestion from diverse sources into Snowflake, ensuring real-time data integration and synchronization.• Manipulated semi-structured and unstructured data using Snowflake’s native support for JSON and XML formats, transforming raw data into structured formats within Bronze-Silver-Gold Zones for improved data analysis and processing.• Supported the development and orchestration of ETL workflows using Fivetran and Snowflake’s Snowpipe, ensuring continuous data loading and transforming data to meet business reporting requirements.• Processed real-time streaming data using Snowflake’s external functions, enabling efficient integration with third-party services and external APIs, while supporting downstream data processing for analytics and reporting.• Enhanced performance of data workflows by leveraging Snowflake’s auto-clustering and partitioning techniques to optimize query execution, significantly reducing data processing time and improving system performance.• Assisted in designing and implementing a Star schema within Snowflake’s data warehouse, optimizing data retrieval and analytical query performance for business reporting and decision-making.• Implemented Slowly Changing Dimensions (SCD) Type 1 and Type 2 in Snowflake to track historical data changes, ensuring data integrity and providing accurate and reliable insights for business analytics.• Utilized DBT (Data Build Tool) for managing transformation workflows in Snowflake, improving collaboration, version control, and modular data transformations for scalable pipeline development.

India

- Built and maintained scalable data ingestion pipelines using Azure Data Factory, Databricks, and Linked Services/Datasets, ensuring data accuracy and consistency across environments.- Led migration of on-premise databases and data (marketing, sales, audit) from third-party vendors to Azure Data Lake, Azure Blob Storage, and Azure SQL Database.- Developed ETL processes to extract, transform, and load data from source systems into Azure Data Storage using a combination of Azure Data Factory, SQL, Spark, Python, and Azure Data Lake Analytics.- Worked with Databricks APIs to extract and manipulate data at the workspace and account levels, applying Spark transformations to streamline data processing.- Processed real-time streaming data using Spark Structured Streaming, enabling immediate insights and improving operational efficiency.- Scheduled and orchestrated Databricks jobs using Azure Data Factory to automate data processing workflows and optimize delivery timelines.- Successfully implemented Unity Catalog to replace Hive, enhancing data governance, lineage tracking, and metadata accuracy across projects.- Optimized Spark jobs by fine-tuning performance through partitioning, caching, broadcast variables, and cluster configurations, leading to significant processing efficiency gains.- Collaborated with data analysts to translate business requirements into technical specifications, designing complex Spark-SQL queries and PySpark code for data transformations.- Developed and documented robust CI/CD pipelines to automate testing and deployment processes, and created detailed runbooks for seamless production handovers.

Hyderabad, Telangana, India

• Supported the implementation of data pipelines using Apache Sqoop to ingest supply chain data from various sources into HDFS. Utilized Apache Spark (RDDs, DataFrames, SQL) for data transformation, cleaning, and aggregation to prepare data for analysis.• Involved in Requirement gathering, Business Analysis and translated business requirements into technical design in Hadoop and Big Data.• Involved in SQOOP implementation which helps in loading data from various RDBMS sources to Hadoop systems.• Helped in designing and implementation of data storage solutions using HDFS, ensuring efficient and secure storage of large-scale supply chain data.• Leveraged Apache Spark for processing and unifying supply chain data from various platforms, enabling the creation of consolidated customer profiles.• Utilized YARN for effective cluster management, optimizing resource allocation and job scheduling across the data processing environment.• Optimized Hive queries using partitioning and bucketing to reduce execution times and improve overall data retrieval for large datasets• Employed advanced Spark SQL, DataFrames, and RDDs to perform complex data transformations and enrichments on data stored in HDFS.