Krishna C Email & Phone Number

Atlanta, Georgia, United States

•Led migration, rewriting, and data matching of complex SQL code between Teradata and Snowflake, using manual methods and automated tools.•Extensive experience with data warehousing solutions in Teradata and Snowflake, ensuring smooth transitions and effective data management.•Utilized DBeaver for SQL management and Python for data analysis and automation, supporting the migration process and enhancing workflow efficiency.•Optimized ETL/ELT jobs and workflows to enhance performance… Show more •Led migration, rewriting, and data matching of complex SQL code between Teradata and Snowflake, using manual methods and automated tools.•Extensive experience with data warehousing solutions in Teradata and Snowflake, ensuring smooth transitions and effective data management.•Utilized DBeaver for SQL management and Python for data analysis and automation, supporting the migration process and enhancing workflow efficiency.•Optimized ETL/ELT jobs and workflows to enhance performance in AWS Cloud environments.•Conducted comprehensive unit and functional testing of repointed ETL/ELT pipelines to maintain data integrity and performance.•Identified and documented SQL dialect differences between Teradata and Snowflake for smooth code migration.•Contributed to the SHIFT™ technical backlog with technical expertise and feedback to enhance internal product development at JPMC.•Implemented SHIFT™-generated code in the migration project to meet internal requirements and standards.•Collaborated within agile development cycles and worked efficiently with Jira for project management and workflow optimization to ensure timely project delivery. Show less

Atlanta, Georgia, United States

•Collaborated with team members and industry partners in the Insight Lab to solve strategic partners' business problems using our big data applications.•Created and executed data pipelines with Hadoop, Spark, Python, and Scala to effectively handle large volumes of data from diverse sources.•Used AWS S3, AWS Glue and AWS Redshift to build a reliable infrastructure for storing, processing, and analyzing data.•Utilized AWS Lambda for serverless automation of data processing tasks… Show more •Collaborated with team members and industry partners in the Insight Lab to solve strategic partners' business problems using our big data applications.•Created and executed data pipelines with Hadoop, Spark, Python, and Scala to effectively handle large volumes of data from diverse sources.•Used AWS S3, AWS Glue and AWS Redshift to build a reliable infrastructure for storing, processing, and analyzing data.•Utilized AWS Lambda for serverless automation of data processing tasks, enhancing scalability, and reducing operational overhead in workflows.•Implemented ETL/ELT processes with PL/SQL querying, transforming raw data to usable formats aligned with business needs. •Utilized Databricks to enhance the efficiency and scalability of big data processing tasks by developing and executing Spark/Scala and Python scripts.•Successfully transitioned data models from Informatica to PySpark within Azure Databricks, resulting in a 30% improvement in data processing efficiency and a 20% reduction in maintenance overhead.•Used ML techniques such as logistic regression, multivariate regression, K-Means clustering, and anomaly detection to find patterns in data, predict outcomes, and provide valuable insights to industry partners.•Developed interactive dashboards and reports using Power BI to visualize data insights and support data-driven decision-making processes. Show less

India

•Led Azure-based data architecture project for healthcare labs, managing Azure Data Lakes and integrating PySpark ML models to accelerate research insights and innovation from lab data.•Implement Azure Data Factory pipelines to gather data from diverse sources, orchestrating the extraction process and loading it into target destinations.•Managed Azure Data Lake and Azure Data Factory for scalable storage, processing, and analytics of structured and unstructured lab data, resulting in a… Show more •Led Azure-based data architecture project for healthcare labs, managing Azure Data Lakes and integrating PySpark ML models to accelerate research insights and innovation from lab data.•Implement Azure Data Factory pipelines to gather data from diverse sources, orchestrating the extraction process and loading it into target destinations.•Managed Azure Data Lake and Azure Data Factory for scalable storage, processing, and analytics of structured and unstructured lab data, resulting in a 30% reduction in infrastructure management overhead.•Deployed Snowflake as a cloud-based data warehouse solution, leveraging its elasticity and scalability to efficiently store and analyze healthcare lab data.•Applied SQL Server tools, including SSIS and SSRS, for conducting ETL processes and generating reports on structured lab data stored in SQL and NoSQL databases.•Used the Hadoop and Spark frameworks, along with Python, to effectively analyze massive amounts of lab data to extract valuable insights and trends.•Integrated real-time data streaming into Power BI for dynamic monitoring of lab processes, enhancing decision-making and operational efficiency. •Implemented CI/CD pipelines to automate deployment and testing, ensuring faster delivery and improved quality, leading to a 50% reduction in deployment time. Show less

India

•Employed REST APIs and web scraping tools like Selenium and Beautiful Soup to gather data from e-commerce platforms, encompassing customer logs, product catalog details, and user behavior data.•Implemented Airflow for orchestrating and automating data pipelines, ensuring seamless extraction, transformation, and loading of e-commerce data into the Data Lake.•Leveraged SQL, Python, and SAS to conduct data analysis on large, complex datasets, ensuring data integrity through rigorous data… Show more •Employed REST APIs and web scraping tools like Selenium and Beautiful Soup to gather data from e-commerce platforms, encompassing customer logs, product catalog details, and user behavior data.•Implemented Airflow for orchestrating and automating data pipelines, ensuring seamless extraction, transformation, and loading of e-commerce data into the Data Lake.•Leveraged SQL, Python, and SAS to conduct data analysis on large, complex datasets, ensuring data integrity through rigorous data validation techniques and supporting data-driven decision-making with advanced analytics.•Explored and leveraged knowledge graphs to analyze intricate connections between products, customers, and interactions.•Developed a Model-based Collaborative Filtering System to recommend products to customers, resulting in a 10% increase in cart value; contributed to enhancing average session time from 1.3 to 2.4 minutes.•Conducted data analysis and created visualizations in Tableau, enabling stakeholders to monitor critical performance metrics and respond proactively to market dynamics.•Applied time series analysis methods to examine patterns and trends in customer behavior over time.•Migrated data from on-premises storage to AWS S3 buckets for improved scalability and accessibility in data analysis processes. •Conducted sentiment analysis on customer reviews using the TF-IDF vectorization technique in NLP.•Utilized Linear and Logistic Models (LLMs) for predictive modeling, resulting in a 25% increase in accuracy in forecasting customer preferences and behavior. Show less

Master'S Degree, Data Science And Analytics, 3.95/4.0

Coursework includes : -> Database Systems -> Big Data Programming -> Fundamentals of Data Science -> Machine Learning -> Bayesian.

Bachelor'S Degree, 8.06/10