Saikrishna Katta

Project - USMCA Feb 2024 - Apr 2025

• Used GCP buckets as the initial storage layer for raw data coming from various sources like Flat files, RPA, file servers, SAP.
• Implemented ETL pipelines by transforming Alteryx workflows into Spark Scala jobs to process raw data into structured formats.
• Utilized Scala, SQL and conducted comprehensive performance analysis and optimization exercises for existing code improving efficiency.
• Monitored and debugged dataproc jobs using logs and metrics, identifying and resolving bottlenecks in data pipelines.
• Automated the jobs and loaded transformed data into BigQuery using airflow.
• Ensured accurate and timely availability of data for Power BI dashboards by monitoring and optimizing ETL pipelines.

Project - Trade Vault Aug 2022- Jan 2024

• Built scalable data warehousing solutions using Snowflake, integrating diverse data sources, including APIs, third-party tools, and on-premises systems, to centralize business intelligence reporting.
• Implemented SQL-based ETL processes directly in Snowflake using native features, such as Streams , Tasks, and Stored Procedures, to ingest and transform trade data.
• Created scalable data ingestion pipelines to load structured and semi-structured trade data (e.g., JSON, CSV, and Parquet) into Snowflake's staging tables for processing.
• Utilized Snowflake's Time Travel and Zero-Copy Cloning features to manage historical data and support back-testing and audit requirements in the trade vault.
• Enhanced data security by implementing role-based access control and utilizing Snowflake's data masking for sensitive trade information.
• Google Analytics and Big Query are leveraged to track and analyze user behavior, including calculating user time on site and engagement metrics across various web pages and product categories.

• Worked alongside the Data Architect and DevOps team to assist in the development and maintenance of ETL processes for data extraction, transformation, and loading.
• Applied data cleansing (e.g. flagging invalid, duplication) for ingested tables.
• Built reusable Databricks Notebooks for data transformations, ensuring modularity and reducing maintenance efforts for complex data workflows.
• Implemented Delta Lake in Azure Databricks to enable ACID transactions, time travel, and schema enforcement, ensuring robust data governance and reliability.
• Automated data pipeline scheduling and monitoring using Azure Data Factory, integrating with Databricks Jobs API for seamless orchestration.
• Assisted in troubleshooting and resolving data-related issues to ensure data quality.