User details
SK
SK
sasidhar reddy kunduru
@sasidhar_reddy_kunduru_61x6w5r
Data Analyst
Nyack, NY, United States
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Member since: Jan 21, 2025• Data Analyst with 3+ years of experience, specializing in Python, R, SQL, Bash, and Shell Script, with expertise in utilizing Tableau, Power BI, and QlikView for advanced reporting and data visualization, as well as data manipulation, data governance, and data modeling across various domains.
• Comprehensive understanding of Software Development Life Cycle (SDLC) and methodologies like Agile and Waterfall, ensuring efficient delivery of data-driven solutions.
• Proven ability to work with relational and cloud-based databases such as MySQL, PostgreSQL, Snowflake, AWS Redshift, and Azure SQL Database, with demonstrated expertise in writing optimized SQL queries, creating views, stored procedures, and performing ETL operations.
◦ Skilled in extracting, cleaning, and transforming large datasets using Python (pandas, numpy), R (dplyr, tidyr), and SQL, ensuring data integrity and consistency for business intelligence tasks.
◦ Proficient in data visualization tools such as Tableau, Power BI, and matplotlib to design compelling dashboards, charts, and reports tailored to client and stakeholder needs.
• Adept at identifying trends, patterns, and actionable insights from complex datasets through advanced analytics, supporting data-driven decision-making across financial, healthcare, and automotive sectors.
◦ Strong knowledge of data warehousing concepts, ETL pipelines, and cloud technologies, including AWS (S3, Lambda, DynamoDB), Azure (Data Factory, Synapse), and GCP (BigQuery) for scalable data processing and storage solutions.
◦ Experienced in text analytics and machine learning basics, including regression, decision trees, and clustering algorithms, using Python frameworks like Scikit-learn and TensorFlow.
◦ Collaborated effectively with cross-functional teams to gather business requirements, translate them into actionable data tasks, and deliver insights through interactive dashboards and detailed reporting.
◦ Skilled in big data technologies like Hadoop, Spark, and Databricks to analyze and process high-volume datasets, ensuring efficient real-time insights and reporting.
◦ Strong attention to detail and ability to manage multiple projects under tight deadlines, maintaining high standards of data quality and accuracy.
• Developed and optimized data pipelines using Apache Airflow, DBT, and Informatica to integrate clinical trial data across cross- functional teams, ensuring timely data availability for analysis.
• Collaborated with stakeholders to design interactive dashboards in Tableau, Power BI, and QlikView, utilizing Snowflake and Azure Synapse Analytics to deliver actionable insights on clinical trial progress.
• Utilized Python (pandas, numpy) and R (dplyr, tidyr) to perform advanced data manipulation and cleaning, enabling accurate reporting and seamless handoffs between teams.
• Executed SQL queries across relational databases like MySQL, PostgreSQL, and Snowflake to extract and aggregate large datasets, supporting cross-team analysis of clinical data.
• Leveraged cloud services such as AWS S3, Azure Data Factory, and Google BigQuery to store, retrieve, and process clinical trial data, ensuring high availability and security.
• Enhanced collaboration workflows by building ETL processes in Talend Studio and Azure Data Factory, streamlining data transformation and delivery for multiple teams.
• Created custom visualization solutions using matplotlib, seaborn, and ggplot2 for team presentations, facilitating effective communication of complex trial data across departments.
• Ensured data security and compliance by implementing IAM roles and secret management tools (AWS IAM, Azure Key Vault) to safeguard sensitive clinical trial information shared across teams.
• Built interactive dashboards using Tableau, Power BI, and QlikView to visualize portfolio performance, market trends, and risk metrics, leveraging Snowflake and Google BigQuery as data sources.
• Developed automated data pipelines using Apache Airflow, DBT, and Talend Studio to integrate and preprocess data from AWS Redshift, Azure Synapse Analytics, and MongoDB for reporting purposes.
• Performed advanced data manipulation and exploration using Python libraries such as pandas, numpy, and regex, alongside SQL queries to extract actionable insights from complex datasets stored in relational and cloud databases like MySQL, PostgreSQL, and DynamoDB.
• Created custom visualizations with matplotlib, seaborn, ggplot2 (Python), and D3.js for in-depth analysis of investment performance and client portfolio trends.
• Streamlined ETL workflows by implementing Informatica, SSIS, and AWS Glue to efficiently transform raw data into structured formats like CSV, JSON, Parquet, and XML for seamless integration into visualization tools.
• Enhanced data security and compliance by implementing IAM roles and secret management solutions via AWS IAM, Azure Key Vault, and Google Cloud Storage for handling sensitive client data in reporting workflows.
• Optimized report generation using advanced Excel features such as Pivot Tables, Macros, and Power Query to create dynamic, client-ready presentations and insights.
• Integrated cloud and big data technologies like Hadoop, Spark, and Databricks to process high-volume datasets, enabling faster and more accurate reporting for stakeholders on multi-platform systems including AWS, Azure, and GCP.
• Utilized Python (pandas, numpy) and SQL to process and analyze large-scale EV battery performance data, identifying patterns in charging cycles and energy efficiency.
◦ Developed interactive dashboards in Tableau and Power BI to visualize energy consumption, battery lifespan, and vehicle range metrics for cross-functional stakeholders.
• Automated ETL workflows using Apache Airflow, Talend Studio, and Informatica to integrate data from cloud platforms like Snowflake and AWS Redshift into analytics pipelines.
◦ Employed machine learning algorithms (Regression, Random Forest) in Python using Scikit-learn to predict battery degradation and optimize EV performance metrics.
• Processed high-volume sensor data from connected EVs using Apache Kafka, Spark, and Databricks, enabling real-time analysis and reporting.
◦ Ensured data security and compliance by implementing AWS IAM roles, Azure Key Vault, and encryption techniques to handle sensitive EV telematics data.
◦ Performed data transformation and cleaning using regex, pyjanitor, and R (dplyr, tidyr), preparing datasets for advanced analysis of vehicle energy consumption patterns. . Leveraged cloud-based storage solutions like AWS S3, Azure Blob, and Google BigQuery to store and retrieve EV performance datasets for scalable analytics solutions.