Tejaswi R. Email and Phone Number

- Identifying and documenting business logic to clean, normalize, and model incongruent data, while assisting in developing metrics, reports, dashboards, and visualizations based on client analytics requirements. Performing exploratory data analysis, uncovering trends, and communicating insights through reports and presentations.- Designing and implementing large-scale distributed data systems and pipelines, with a focus on scalability, low-latency, and fault-tolerance. Developing reusable frameworks for ingesting data in Databricks and engaging in query tuning, performance optimization, and troubleshooting big data solutions.

- Building infrastructure for optimal extraction, transformation, and loading (ETL) of data from various sources using SQL and Azure big data technologies. Constructing analytics tools to derive actionable insights on customer acquisition, operational efficiency, and other key business metrics.- Collaborating with stakeholders (Executive, Product, Data, and Design teams) to address data-related technical issues and support infrastructure needs. Working closely with data and analytics experts to enhance the functionality of data systems.

Interactive Data Exploration and Visualization with Tableau | Data Analyst Developed a data exploration and visualization platform using Tableau to analyze large datasets in ecommerce industry.Designed and implemented ETL processes using PySpark and Apache Spark for data preprocessing and aggregation.Utilized SQL for querying and managing databases (e.g., MySQL, Oracle, SQL Server) to extract relevant data for analysis.Integrated Tableau with cloud platforms such as Microsoft Azure and Azure Databricks for accessing and processing data.Created interactive dashboards and visualizations in Tableau to enable users to explore and gain insights from the data.Real-Time Data Processing Pipeline on Microsoft Azure | Data Engineer Designed and implemented a real-time data processing pipeline using Apache Spark on Microsoft Azure.Utilized PySpark and Scala for ETL processes, including data ingestion, transformation, and loading into Azure Data Lake Storage (ADLS).Integrated Azure Data Factory (ADF) for orchestrating and scheduling data workflows.Implemented data visualization dashboards using Power BI to monitor pipeline performance and visualize key metrics.Collaborated with team members to optimize pipeline efficiency and scalability.

- Spearheaded the development of objects and validations for accurate data processing, while providing post-deployment support to optimize system performance. Leveraged Python and PySpark for intricate data processing, enhancing efficiency in handling large datasets.- Employed Power BI to create Tabular Cubes and advanced reports, enabling stakeholders to gain actionable insights. Managed Azure Data Factory jobs, performed performance tuning on workflows, and documented technical specifications and system architecture to support project implementation.

Orchestrated end-to-end ETL processes encompassing data acquisition, transformation, and loading, ensuring seamless integration of diverse data sources to facilitate comprehensive analysis.Employed Azure Data Factory to design and manage robust data pipelines, streamlining data movement and automating workflows to enhance operational efficiency.Leveraged Azure Synapse Analytics to establish scalable data warehousing solutions, facilitating advanced analytical processing and enabling timely insights generation.

Utilized Azure SQL Database as a reliable storage solution and query platform for structured data, ensuring optimal performance and accessibility for data-driven decision-making.Implemented sophisticated data modeling techniques, including star schema, to structure data for efficient analysis and reporting, enabling quick identification of key business insights.Prioritized data quality and integrity by implementing rigorous data cleansing and validation procedures, ensuring accuracy and reliability of analytical outputs.Implemented robust security measures such as role-based access control (RBAC) and encryption, safeguarding sensitive data and ensuring compliance with regulatory requirements.