shruti chikode

- Azure Pipelines: Designed scalable data pipelines for batch and streaming, integrating Azure Container - PostgreSQL: Built ETL/ELT pipelines on Docker, optimizing big data handling. SQL Optimization: Enhanced SQL query performance - API Extraction: Integrated data from APIs into ETL pipelines. - DBT: Developed transformation workflows, ensuring data quality. - Azure Data Factory: Created Python pipelines for SQL databases and Azure Data Lakes - Data Vault 2.0: methodologies for scalable ETL architectures.

- Migration: Leveraged Azure Cloud, PySpark, SparkSQL, and Databricks for big data batch processing. - Apache Airflow: Scheduled and triggered workflows efficiently which is useful for managing complex workflows. - Data Infrastructure: Designed cloud infrastructures using Hadoop and Spark for OLTP and OLAP systems. - Azure Synapse Analytics: Developed data warehouses for banking migration, enhancing fraud detection and prevention. - Agile Methodology: Experienced in SCRUM practices.

- Data Preprocessing: Cleaned data using Python, PySpark, T-SQL and NoSQL - Data Storage: Designed data structures in JSON, Delta-Parquet and ORC formats - Predictive Modeling: Developed models with NumPy, pandas, Scikit-Learn, SciPy and PyTorch for optimization - Analysis: Conducted analysis on large datasets for outliers, feature selection and predictions - Data Visualization: Created visualizations using Plotly, Matplotlib and Seaborn - Time Series Analysis:Analyzed COVID and IPL datasets with TensorFlow

Client Interaction: Oversaw banking project lifecycle from requirements to implementation. - Query Optimization: Enhanced performance of views and complex relational data queries. - RDBMS Objects: Developed PL/SQL stored procedures, tables, materialized views, packages, and triggers. - Reporting & Dashboards: Created banking reports and dashboards using Power BI - On-Site Support (Bahrain): Resolved user issues and managedsupport Tickets