Data Engineer with 4+ years of experience in designing scalable data pipelines, analytical thinking. A Computer Science graduate with a 4.0 GPA, skilled in PySpark, Snowflake, dbt, and Dagster. Proficient in web scraping…
Data Engineer with 4+ years of experience in designing scalable data pipelines, analytical thinking. A Computer Science graduate with a 4.0 GPA, skilled in PySpark, Snowflake, dbt, and Dagster. Proficient in web scraping, data validation, and creating Tableau dashboards. Committed to optimizing system performance and data quality through innovative solutions. Ready to drive business growth and contribute to impactful data-driven initiatives.
Designed and implemented a scalable data processing pipeline using PySpark to handle and analyze large datasets efficiently.
Improved data processing speed, reliability, and scalability while ensuring data quality and accuracy.
Resolved data issues if any.
Utilized PySpark to ingest large volumes of data from CSV files, JSON files, and relational databases.
Streamlined ETL processes to clean, transform, and normalize data.
Reduced manual effort and data latency.
Implemented window functions to perform advanced aggregations and calculations.
Used Data Frame operations to filter, group, and join large datasets.
Employed UDFs (User Defined Functions) to apply custom transformations and logic.
Configured Fivetran to automate the extraction of data from various sources and load it into the data warehouse.
Leveraged Hightouch to sync transformed data from the warehouse to operational systems.
Ensured real-time availability.
Scheduled and monitored data pipeline jobs.
Ensured timely and reliable data acquisition and processing.
Improved workflow efficiency by leveraging Dagster's modular architecture for easy maintenance and scalability.
Leveraged Snowflake's capabilities to manage and optimize data storage and processing.
Designed and implemented data models using dbt (data build tool).
Transformed raw data into meaningful insights.
Prepared detailed reports using Tableau.
Processed and analyzed terabytes of data.
Improved processing efficiency.
Improved data quality and accuracy.
Reduced processing times by 20%.
Enabled quicker turnaround on data initiatives.
Developed and maintained technical specifications and documentation for all data pipelines and workflows.
Utilized Git and GitLab for version control and Bitbucket for repository management.
Employed Node.js and JavaScript (JS) for backend scripting and integration tasks.
Conducted code reviews to ensure adherence to best practices and standards.
Developed use cases and technical architecture documents to guide data engineering projects.
Performed troubleshooting and issue resolution for data pipeline problems.
Ensured minimal downtime.
Participated in continuous integration and continuous deployment (CI/CD) processes to streamline development and deployment.
Collaborated with professor.
Led seminars on deep learning with PyTorch.
Led seminars on natural language processing with NLTK and Spacy.
Led seminars on reinforcement learning.
Seminars were well-received.
Increased student engagement.
Participated in research projects alongside professor.
Applied machine learning models to solve complex problems.
Enriched understanding.
Provided real-world examples to share with students.
Developed hands-on labs and assignments involving Jupyter Notebooks.
Designed a data scraping and analysis pipeline using web scraping tools.
Created interactive dashboards and pivot tables using visualization software.
Used Playwright to automate browser interactions and scrape dynamic content.
Employed Beautiful Soup to parse HTML and extract relevant data from web pages.
Leveraged request library to send HTTP requests and retrieve data via APIs.
Implemented Selenium to handle complex web scraping tasks involving JavaScript-rendered content.
Cleaned and transformed the scraped data for analysis using Python and Pandas.
Designed intuitive dashboards with filters and drill-down capabilities for deeper insights.
Integrated data from multiple sources to provide a comprehensive view of the market.
Shared dashboards with stakeholders to facilitate data-driven decision-making.
Provided recommendations.
Improved the efficiency of data collection and analysis.
Enabled the company to better understand customer preferences and adapt to market trends more effectively.
Designed a data scraping and analysis pipeline using web scraping tools.
Created interactive dashboards for visualization.
Used Playwright to automate browser interactions and scrape dynamic content.
Utilized Beautiful Soup to parse HTML and extract data from web pages.
Leveraged request library to send HTTP requests to retrieve data from APIs.
Implemented Selenium to handle complex web scraping tasks involving JavaScript-rendered content.
Cleaned and transformed the scraped data for analysis using Python and Pandas.
Designed intuitive dashboards with filters and drill-down capabilities for deeper insights.
Integrated data from multiple sources to provide a comprehensive view of the market.
Shared dashboards with stakeholders to facilitate data-driven decision-making.
Improved the efficiency of data collection and analysis.
Enabled the company to better understand customer preferences and adapt to market trends more effectively.
Led the migration of data from legacy systems to modern data platforms.
Ensured smooth transition and minimal downtime.
Assisted in deployment and release management for data analytics solutions.
Implemented business intelligence solutions to enhance DDDM.
Ensured compliance with MISRA standards in data processing and handling.
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