Sai Krishna Email & Phone Number

Columbus, Ohio, United States

• Proficient in crafting and designing multiple data pipelines, overseeing the complete ETL and ELT process for data ingestion and transformation within the Google Cloud Platform (GCP).
• Successfully set up a Continuous Delivery pipeline using Docker and GitHub, streamlining the deployment process.
• Re-platformed AWS EMR-based Spark and Hadoop jobs to GCP Dataproc, leveraging GCP's autoscaling and managed cluster features for optimized resource management and cost control.
• Transitioned logging and monitoring solutions from AWS CloudWatch to Google Cloud Monitoring and Logging to maintain visibility into the migrated data pipelines and ensure performance metrics are equal.
• Developed, deployed, and managed results utilizing Spark and Scala code within a Hadoop cluster hosted on GCP.
• Skilled in leveraging Google Cloud components, Google Container Builders, GCP client libraries, and Cloud SDKs to architect and execute data solutions.

Indianapolis, Indiana, United States

• Migration of an entire Oracle database to BigQuery and employed Power BI for robust reporting. Crafted data pipelines within Google Cloud's Airflow to streamline ETL tasks using a variety of airflow operators.
• Proficient in a range of Google Cloud Platform (GCP) services including Dataproc, Google Cloud Storage (GCS), Cloud Functions, and BigQuery.
• Demonstrated expertise in Google Cloud components, Google Container Builders, GCP client libraries, and Cloud SDKs.
• Actively participated in migrating on-premises Hadoop systems to GCP (Google Cloud Platform).
• Conducted in-depth analysis of data from diverse domains to enable seamless integration into a Data Marketplace.
• Developed Pyspark programs, established data frames, and executed data transformations.

• Leveraged Spark RDD, Data Frame API, Data Set API, Data Source API, Spark SQL, and Spark Streaming alongside SQL and DynamoDB for comprehensive data processing
• Developed Spark applications using both Python and R, including implementing Apache Spark data processing projects to handle data from various RDBMS and streaming sources
• Pioneered a cross-functional team to successfully migrate ETL processes from SSIS to Snowflake, ensuring data integrity and minimal downtime.
• Redesigned data workflows to leverage Snowflake’s powerful SQL capabilities, optimizing performance and reducing processing time.
• Efficiently extracted data from SQL server, Amazon S3 buckets, and internal SFTP, loading them into AWS S3 buckets in a data warehouse context
• Developed Spark jobs for data processing and orchestrated instances and clusters to load data into AWS S3 buckets, thereby creating a DataMart

• Engineered RESTful APIs with Java Spring Boot to orchestrate seamless data extraction via Sqoop, optimizing data retrieval processes for enhanced efficiency and integration within the Hadoop ecosystem.
• Leveraged Talend for data integration, cleansing, and transformation, while using dbt to refine raw data into structured datasets, leading to faster processing times and higher data quality
• Crafted, developed, and maintained Tableau functional reports according to user specifications, ensuring meaningful data visualization
• Deployed Hadoop and Cloudera Distribution for Hadoop (CDH) to optimize the data processing pipeline, including setup, real-time data ingestion Flume, and Spark analytics
• Proficiency in Python and Scala, with a knack for creating user-defined functions (UDF) for Hive and Pig using Python
• Integrated MongoDB with big data processing frameworks like Hadoop and Spark to build end-to-end data pipelines for batch and stream processing