Avinash A Email & Phone Number

I am a Data Engineer and Data Analyst with over 5 years of extensive experience in the IT sector, focusing on Big Data Hadoop, Data Analysis, Data visualization, predictive modelling, ETL development and cloud technologies. My expertise lies in designing, architecting, and developing solutions that ingest, process, and analyze massive and diverse data sets to meet complex business requirements. My proficiency in programming languages such as Python, Java, Scala, R, C#, and SQL has enabled me to implement scalable solutions that ensure high performance.My expertise includes a wide array of technologies and platforms, including Apache Spark, Hadoop, Kafka, and cloud services like AWS and Azure. Throughout my career, I have successfully led numerous large-scale data integration and data processing projects. I have engineered scalable ETL pipelines using Python, Scala, and SQL, ensuring data integrity while migrating data from on-premises to cloud infrastructure. Additionally, I have designed systems for data storage, implemented machine learning algorithms, and created data lake solutions. I am proficient in real-time data processing and stream analytics, having implemented solutions with Apache Kafka and AWS Kinesis to handle massive data flows with low latency.As a Data Analyst, I have employed a range of data analysis techniques, including statistical analysis, predictive modeling, and data visualization. My experience with tools such as Power BI, Tableau, and Cognos Analytics has enabled me to create insightful dashboards and reports, facilitating data-driven decision-making. I have worked extensively on data cleansing, transformation, and aggregation to ensure high-quality datasets for analysis. I have demonstrated a strong ability to collaborate cross-functionally with data scientists, analysts, and business stakeholders to translate complex requirements into actionable insights.Finally, with a strong focus on performance optimization and data governance, I am adept at implementing best practices in data security.

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  University Of Alabama At Birmingham

  4.0