Zhuyijie LU

As a Graduate Data Engineer with a Bachelor's degree in Civil Engineering and a Master's degree in Data Science, I am eager to leverage my technical skills in Python, Power BI, SQL, and Hadoop for the Data Engineer role. My goal is to develop and manage data pipelines to ensure data quality and reliability, supporting informed decision-making and continuous improvement. I have proven experience in data analytics, focusing on business systems functionality and data presentation. I am committed to delivering high-quality results on schedule and am eager to engage with stakeholders to address data needs effectively. My structured problem-solving skills and strong background in data management make me a perfect fit for this job, where I can contribute to innovative and impactful data solutions.

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• Proficient in designing and analyzing structural systems using software such as AutoCAD, STAAD.Pro, and SAP2000, ensuring safety and stability in construction projects.
• Advanced in transportation engineering, including traffic flow analysis and road design, using software such as Synchro and Civil 3D.
• Competent in utilizing GIS (Geographic Information Systems) for spatial analysis and mapping, enhancing planning and decision-making processes.
• Proficient in developing and optimizing databases and data lakes using MySQL, Oracle, and Hadoop, ensuring efficient data storage and retrieval.
• Proficient in utilizing Python and R for data analysis, manipulation, and automation, enabling streamlined and efficient workflows.
• Advanced in creating data visualizations with Power BI and Tableau, transforming complex datasets into actionable insights for stakeholders.
• Proficient in designing and maintaining SQL queries to extract, analyze, and manage data effectively, ensuring accurate and relevant data outputs.
• Experienced in employing Hadoop for managing and processing large-scale data, leveraging its distributed computing capabilities.
• Experienced in conducting data migration processes to ensure seamless transitions and integration of data across systems.

• Machine Learning Project, 07/2023, 06/2024, Conducted data exploratory analysis to understand underlying patterns and relationships within the dataset., Performed data preprocessing, including cleaning and normalization, to prepare the dataset for modelling., Engineered features to enhance model performance, utilizing techniques like SMOTE to handle class imbalances., Developed and tuned sophisticated machine learning models, including Random Forest, SVM, Neural Networks, and LightGBM., Conducted extensive hyperparameter tuning using randomized grid search and cross-validation to optimize model performance., Created a custom hybrid model that combined the strengths of individual models to enhance prediction accuracy., Performed detailed exploratory data analysis and visualized complex data relationships using heatmaps and boxplots., Managed project progress and team coordination effectively using project management tools like Trello.
• Big Data Processing and SQL Project, 07/2023, 06/2024, Collected diverse data forms and stored them in Hadoop's HDFS for optimal fault tolerance and scalability., Leveraged PySpark for complex data transformations., Employed Hive to facilitate SQL-like querying on structured data., Gained deep analytical insights into user behaviour and system performance., Designed efficient ETL processes and optimized data query performance., Utilized Python for scripting and automation within a distributed computing environment.
• Data Visualisation project, 07/2023, 06/2024, Analysed the relationship between property transactions and suburb liveability in Melbourne., Mapped average transaction prices and demographic data to create liveability heat maps., Provided insights for property tenants, real estate agents, and investors to facilitate informed decision-making., Created visualizations of macrotrends of property prices from 2010 to 2022., Ranked suburb property prices and liveability scores., Designed visualizations using interactive elements, consistent colour palettes, and storytelling techniques., Implemented scatter plots, line graphs, bar graphs, and pie charts using the R package 'plotly' for interactive data exploration., Demonstrated expertise in data visualization using R, particularly with the plotly package., Showcased proficiency in mapping and geospatial analysis through the creation of liveability heat maps., Designed user-centric visualizations employing colour theory and interactive elements.
• Data Wrangling and processing Project 1, 01/2023, 07/2023, Identified and corrected inaccuracies within various data columns across multiple CSV files., Cleansed the 'date' field in three provided CSV files to ensure consistency and accuracy., Applied various data cleansing techniques to maintain data integrity and accuracy., Utilized Python, particularly pandas, for data manipulation and cleansing tasks., Analysed data for errors and applied logical solutions to rectify inconsistencies.
• Data Science Project, 07/2022, 06/2023, Used Shell commands to inspect, filter, and manipulate CSV files, extracting specific data points and generating statistics., Scraped crude oil prices from MacroTrends and population data from Australian Population Statistics using web scraping techniques., Cleaned, normalized, and visualized data using the ggplot2 package in R., Loaded and cleaned a large property transaction dataset for Victoria., Conducted in-depth exploratory data analysis to visualize trends and patterns in property transactions., Demonstrated proficiency in Shell scripting for data inspection and manipulation., Showcased expertise in web scraping using the 'rvest' library in R., Utilized 'dplyr' and 'tidyverse' for data manipulation and cleaning, including handling missing values and parsing data types., Created insightful bar and line charts and customized plots using ggplot2., Applied advanced R programming skills, including the use of 'lubridate' for date manipulation.