Kiet Do: Aspiring Data Engineer and Analyst

Đỗ Đình Kiệt

Data Analyst

035*******

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Hải Châu, Đà Nẵng

CAREER GOALS

In the near future, I aim to further develop my expertise to become a proficient Data Analyst, strengthening my abilities in data analysis, data visualization, and programming. I seek to work with real-world datasets to uncover meaningful patterns and deliver clear, actionable insights that support data-driven decision-making and contribute tangible value to organizations. PROJECT

CAPSTONE 1 - Personal

Projects

2/5/2025 - 10/5/2025

POSITION: DATA ANALYST

Project name: Customer Personality Analysis & Segmentation Tools & Techniques: Python, Power BI, PCA, K-Means Clustering.

• Analyzed a customer marketing dataset with 2,240 records and 29 features from Kaggle

• Data Analysis & Processing: Cleaned and normalized a dataset of 2,240 customer records, performed Feature Engineering and Label Encoding to optimize data for machine learning models.

• Customer Segmentation: Implemented K-Means clustering integrated with PCA using Python; segmented customers into 4 strategic groups based on demographics and spending behaviors.

• Built 3 interactive Power BI dashboards to analyze customer demographics, purchasing behavior, and campaign performance

Key Results: Identified key customer segments and campaign effectiveness, providing data-driven recommendations to improve targeted marketing strategies GitHub: github.com/dodinhkiet/customer-personality-analysis CAPSTONE 2 - Team Project

(3 members)

9/2025 - 12/2025

POSITION: DATA ENGINEER / DATA ANALYS

Project name: IMDb Movies ETL Pipeline & Analytics Tools & Techniques: Python, Prefect Cloud, Google Cloud Platform (Compute Engine, BigQuery), SQL (BigQuery), ETL, Data Pipeline, Workflow Orchestration.

• Design and implement an automated daily ETL process to collect and process movie data from IMDb.

• Extracted daily updated IMDb datasets (.tsv.gz), cleaned, transformed and normalized data into 9 relational tables, and loaded them into Google BigQuery

• Deployed the pipeline on Google Compute Engine, scheduled to run automatically using cron-based orchestration

• Processed and analyzed over 150,000 movies (2000–2025) to uncover trends and support data-driven insights

• Orchestrated workflows with Prefect Cloud

• Design and develop Tableau dashboards for analyzing film data.

• Developed a Hybrid Movie Recommendation System combining content-based filtering

(TF-IDF, cosine similarity) and IMDb weighted rating formula Key Results: Successfully built an automated ETL pipeline, collecting and processing IMDb data, storing it on Google BigQuery to support data visualization and analysis, uncovering insights, and building a data-driven movie recommendation system. GitHub_Pipeline(ETL): https://github.com/dodinhkiet/imdb-etl-pipeline GitHub_movie recommendation system: https://github.com/dodinhkiet/imdb-movie- recommender

Link_movie recommendation system: https://imdb-movie-recommender-cap- nhom3.streamlit.app/

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Link_TableauPublic: https://public.tableau.com/app/profile/ngoc.chi6322/viz/IMDb_17639 677517520/Dashboard1#1

EDUCATION

2022 - Present

Major: Data science and Business analytics

At: Da Nang University of Economics

GPA: 3.05/4

CERTIFICATE

2025

IBM Data Analyst Professional Certificate – Coursera INTERESTS

• I like playing football and badminton.

• Trekking, playing chess, music.

SKILLS

Programming language:

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)

• SQL

Visualization Tools:

• Power BI, Tableau, Google Data Studio, Excel (Pivot Tables) Database & Data Warehouse skills:

SQL Server, Google Bigquery

Other Knowledge:

• Have knowledge of Machine learning

• Have basic knowledge of Workflow & Cloud Platforms: Prefect, Google Cloud Platform

• Teamwork, Problem-solving, Responsibility

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