Savan Patel

 SAVAN NILESHBHAI PATEL Azure Data Engineer | ETL Developer EMAIL: - CONTACT: - LINKEDIN: https://www.linkedin.com/in/savan-patel21 LOCATION: Toronto, Canada GITHUB: https://github.com/Savan2110 TECHNICAL SUMMARY Nearly 4 years of focused IT experience as an Azure Data Engineer, adeptly utilizing Azure Data Factory, Azure Synapse, Azure Databricks, and various other data analytics platforms to drive efficient data engineering solutions Proficient in utilizing Azure Data Factory to orchestrate and automate data workflows, enabling efficient data movement and transformation across various services and on-premises systems Knowledgeable in building Data lakes using Azure Data Lake Storage and Azure Blob Storage in which Data lakes are repositories for storing vast amounts of structured and unstructured data Integrated data from diverse sources such as Azure Cosmos DB, Snowflake, and Azure HDInsight to ensure smooth data flow and compatibility between different data formats and platforms Implemented Data governance policies to ensure data quality, security, and compliance with regulatory standards using Azure Data Catalog and Azure Data Lake services with activities such as metadata management, data classification, and access control Orchestrated ETL pipelines to automate data ingestion and transformation processes such as extracting, cleansing, standardizing, and mapping data, ensuring timely and accurate client deliver Conducted performance tuning and optimization of Snowflake data warehouse to enhance query execution speed and resource utilization to reduce query execution time and minimize resource usage for repetitive queries Proficiency in writing SQL queries and stored procedures, which are essential for extracting, transforming, and loading data into databases which includes knowledge of working with both Azure SQL Database and MySQL Utilized Azure Databricks and PySpark for ETL (Extract, Transform, Load) processes, integrating data from diverse sources, transforming it as per business requirements, and loading it into Snowflake for analytical purposes Proficient in data analysis migration cleansing governance and transformation using T-SQL Oracle and Azure Data Studio skilled in version control and collaboration with Git and GitHub for efficient data management Monitored and troubleshoot data pipelines and workflows using Azure Monitor and Azure Log Analytics for detecting and resolving issues to ensure system reliability, performance, and availability oriented in managing Azure Key Vault for securely storing and managing keys, secrets, and certificates, enhancing data security across Azure services in Azure Key Vault uptime and zero security breaches in the first year, ensuring data remains protected Collaborated with cross-functional teams using project management tools such as Jira and Confluence to track progress, document requirements, and manage tasks efficiently SKILL MATRIX Azure Data Lake Kafka Cosmos DB Azure Data Bricks Snowflake Azure Purview Azure Data Factory Data warehouse PySpark Azure Stream Analytics Azure Synapse Python Azure SQL Database Blob Storage SSIS Data Migration Azure Monitor Azure Key Vaults Power BI Data Explorer Azure DevOps EDUCATION & CERTIFICATIONS PG Diploma in AI and Data Science, Loyalist College, Toronto, Canada Jan 2022 to Aug 2023 B. Tech in Information Technology, Charusat University, Gujarat, India Jul 2017 to May 2021 DP-900: Microsoft Certified: Azure Data Fundamentals DP-203: Microsoft Certified: Azure Data Engineer Associate AWS Certified Solutions Architect – Associate AWS Certified Cloud Practitioner WORK EXPERIENCE Azure Data Engineer CIBC, Toronto Jul 2022 –Till Date Designed and implemented data models in Azure SQL Database or Azure Synapse Analytics for efficient storage and retrieval of structured and semi-structured data Developed and maintained Azure data pipelines using Azure Data Factory to ingest, transform, and load data from various sources into Azure data lakes and data warehouses Leveraged Azure HDInsight or Azure Databricks for big data processing and analytics tasks, using technologies and optimized performance and scalability of big data solutions by fine-tuning configurations and optimizing code Developed pipelines for batch and real-time data processing using Azure services such as Azure Databricks or Azure Stream Analytics Created interactive dashboards and reports using Power BI to provide insights into data trends and patterns, and integrated with Azure data sources to enable real-time data visualization and analysis Conducted performance tuning and optimization of Azure data solutions to meet business SLAs and ensure data integrity Utilized Azure Logic Apps to automate batch jobs, developed insightful dashboards and visualizations using Microsoft products, and created Spark jobs using Python Created robust data pipelines using Azure Data Factory and Azure Databricks, ensuring seamless data flow from various sources, including IoT devices, event hubs, and on-premise databases Designed and implemented monitoring and alerting solutions using Azure Monitor and Azure Log Analytics to proactively identify and resolve data processing issues Implemented and managed robust security measures, including firewalls, identity, and access management (IAM), encryption, and Role-Based Access Control (RBAC), to strengthen system security Data Engineer INTURE SOFTWARE, India Jun 2021 – Jun 2022 Designed scalable and efficient data models and architectures on Azure platforms such as Azure SQL Database, Azure Cosmos DB, Azure Synapse Analytics, and Azure Data Lake Storage to support both transactional and analytical workloads Implemented data integration solutions to connect disparate data sources and systems, ensuring seamless data flow and interoperability to orchestrate data workflows and dependencies using Azure Data Factory and Azure Logic Apps Automated data integration solutions using Python and Snowflake within Azure Data Factory pipelines to design and implement data workflows to ingest, transform, and load structured and semi-structured data from diverse sources into Snowflake Implemented error handling and logging mechanisms within ADF pipelines to guarantee data integrity and enhance reliability Leveraged advanced optimization techniques in PySpark scripts to enhance the performance of data processing tasks, including partitioning strategies, caching, and parallel processing Optimized the performance and scalability of data solutions by fine-tuning queries, optimizing data storage, and configuring resources such as Azure SQL Database, Azure Cosmos DB, and Azure Synapse Analytics