Data Engineer | GCP | Data Analytics BI/ETL-Bigquery-Jenkins-AtScaleExperience in Cloud (GCP), Configuration management, Infrastructure automation, Continuous Integration and Delivery (CI/CD).Expert in Setting up Continuous Integration (CI) by configuring Build, Code, Deploy and test automation Jobs in Jenkins for different applications and in creation and deployment to automate branch & project creation in Git using (Groovy language) in Jenkins file .Data Integration in large scale implementation environments.Hands-on experience writing scripts in korn shell,batch scripts, sql, pl/sql, python to automate ETL loads to process large and unstructured data sets & to process real time data.Working Knowledge of processing XML Files,JSON files.Hands-on experience in infrastructure management including database management and storage managementDesigned various Jenkins jobs to continuously integrate the processes and executed CI/CD pipeline using Jenkins.Used Git version control to manage the source code and integrating Git with Jenkins to support build automation and integrated with Jira to monitor the commits.Worked with JIRA to follow agile approach.Designed the Data Model and created several Facts&Dimensions based on the reporting needs for the Projects.Worked on designing the table structure based on the Collector data and product data provided by the users.Created Base Tables Stages tables based on the data model and number of source systemsDeveloped BigQuery queries to extract and deliver meaningful insights to stakeholdersImplemented ETL process to streamline the import of data from various sources into BigQuery warehouseEnhanced quality of data insights through implementation of automated data validation processes and improved access to data sources.

Reverse Engineering Proficiency: Demonstrated expertise in reverse engineering tables from BigQuery, covering diverse subject areas and contributing to comprehensive data modeling• Subject Area Expertise: Developed data models for intricate subject areas within the retail domain, including VENDOR DRILL, MY DRILL, SC DRILL, FINANCE CORE, and NURO, enhancing the overall understanding of data relationships and structures• Fact and Dimension Identification: Specialized in designing star schemas by skillfully determining fact and dimension tables. Fact tables, containing numerical measures and large volumes of transactional data, are coupled with dimension tables that store descriptive information, often in a hierarchical structure• Collaborative Data Design: Collaborated with cross-functional teams to design and implement data models that met business requirements, ensuring alignment with organizational goals and objectives.• Tool and Methodology: Utilized SQLDBM for reverse engineering from BigQuery, employing a methodical approach to ensure accurate and insightful data models.• Complex Table Analysis: Conducted in-depth analysis of over 500 complex tables, extracting crucial join condition and also getting the column names from complex subquiries for the purpose of linking the tables while data modeling.• Data Governance and Metadata: Enforced data governance practices during the creation of data models, ensuring adherence to standards and regulations. Incorporated metadata details into data models, enhancing documentation and providing valuable information for data lineage and analysis

Schema Design and Implementation: Designed and implemented database schemas tailored to the requirements of the retail project, ensuring optimal data organization and retrieval.• Dataset Creation: Created comprehensive datasets to facilitate efficient data storage and retrieval, aligning with project objectives and industry best practices.• Table Development: Developed tables to capture and store relevant retail data, adhering to normalization principles and ensuring data integrity.• Advanced Joins: Implemented advanced join operations to connect and retrieve data from multiple tables, enhancing the depth and breadth of analytical insights.• Complex Subqueries: Employed complex subqueries to address specific project requirements, optimizing data extraction and analysis for improved decision-making.• Alias Usage: Utilized aliasing techniques to enhance query readability and simplify complex SQL statements, promoting maintainability and collaboration. • Cross-Dataset Integration: Successfully connected and integrated different datasets within a single project, facilitating the creation of unified and enriched data outputs.• Data Dissemination: Engineered processes to connect various tables and datasets, generating refined outputs to meet project goals and reporting needs.• Query Optimization: Implemented query optimization strategies to enhance overall database performance, ensuring efficient data retrieval even with large datasets• Collaborative in Agile: Collaborated with cross-functional Agile teams to understand project requirements,participating in Agile ceremonies and ensuring SQL solutions aligned with business objectives.• Document: Maintained through documentation for SQL code following Agile documentation practices, ensuring code consistency, readability, and adherence to industry standards.• Data Quality Assurance: Contributed to data quality assurance processes within Agile cycles, ensuring accuracy and reliability of SQL queries and outputs.

Stored Procedures: Contributed in developing and implementing stored procedures, streamlining data processing for efficient information retrieval and management.• Views and Functions: Contributed to the creation of database views and implemented functions, enhancing data accessibility and modular code structures.• Trigger Logic: Worked on trigger development, gaining exposure to logic implementation that automatically responds to predefined events.