Associate Data Engineer with experience in SQL, C#, .NET, Apache Spark, PySpark, SparkSQL, Azure DevOps, AWS, Tableau, Power BI, Kafka, Jira, and Scrum Agile methodologies. Also experienced as a Graduate Assistant and Da…
Associate Data Engineer with experience in SQL, C#, .NET, Apache Spark, PySpark, SparkSQL, Azure DevOps, AWS, Tableau, Power BI, Kafka, Jira, and Scrum Agile methodologies. Also experienced as a Graduate Assistant and Data Analyst Intern, with work involving predictive analysis, dashboarding, ETL, machine learning, and data preprocessing.
Designed dynamic programming lab sessions for a class of 60 students, facilitating hands-on learning experiences.
Conducted regular assessments and feedback surveys to identify students' strengths, weaknesses, and areas of interest in the course.
Integrated collected data with student information using MySQL and applied clustering techniques with python to extract insights through predictive analysis.
Collaborated closely with professors and management to implement adaptive learning strategies by presenting insights through Power BI reports.
This initiative led to a 62% average grade improvement for a cohort of 40 students.
Coordinated with dev and test team to code crucial API’s using SQL, C# MVC architecture in .NET framework and designed CI/CD pipelines using Azure DevOps services, to reduce deployment errors and manual tasks by 70%.
Developed Apache Spark applications in PySpark and SparkSQL to pre-process client data and store it onto Amazon S3 bucket.
Connected AWS Athena and QuickSight to cloud data sources to create insightful dashboards that auto-refresh on data updates.
Prioritized deliverables for each sprint and deployed software changes frequently using Git for version controlling and Jenkins for CI/CD pipelines resulting 50% decrease in deployment time.
Effectively contributed to Scrum Agile software methodologies for project timelines, business requirements, scrum meetings, testing, release management, and ongoing support, resulting in a 73% improvement in project delivery.
Managed and prioritized data-related tasks and bug reports using Jira, resulting in efficient issue resolution on time.
Collaborated with the data team to collect customer support data from companies chart bot, emails and APIs using Kafka.
Engineered an efficient Apache Spark pipeline to automate preprocessing tasks for text data, such as removing stop words, special characters, and normalizing text through stemming and lemmatization; resulted 50% reduction in data preprocessing time and enhanced accuracy of text tokenization for advanced analysis.
Conducted feature engineering to construct a new output variable using VADER from NLTK library.
Developed a supervised machine learning algorithm with 87% accuracy classifying tokens based on the calculated compound score.
Streamlined data reporting processes and improved data accessibility for customer support teams by developing and maintaining automated Tableau dashboards, focused on categories falling into the negative class.
Performed A/B Testing showed an improved customer satisfaction by 36%.
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