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Data Engineer at Hyundai or Google.

Organize and clean data, predict forecast.

Work in Singapore

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3 years: Learn coding, work in preferred company.

5 years: Lead small projects

10 years: Senior Data Engineer

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Important skill set

Coding (Python, SQL)

Problem-solving

Communication

Lifelong learning, etc...

Qualitfication:

Bachelor's or Master's Degree in fields like Computer Science, Industrial Engineering, Mathematics, or related areas.

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AI helps with simple tasks

Humans needed for decisions & problem-solving

Not in danger if I keep learning

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A dream job could be as a Data Engineer at a top tech company like Google. Responsibilities would include building data pipelines, optimizing databases, and working with big data tools. Preferably working in Singapore for career growth, but open to overseas opportunities.

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3 years: Gain hands-on experience as a Data Engineer, working on data pipelines, ETL processes, and cloud platforms.

5 years: Move into a senior or lead Data Engineer role, specializing in big data, machine learning pipelines, or real-time analytics.

10 years: Transition into a Data Architect or Engineering Manager role, designing large-scale data systems and leading teams.

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The most important skills for a Data Engineer include:

Programming: Proficiency in Python, SQL, and Scala.

Data Processing: Experience with ETL tools, Apache Spark, and Kafka.

Database Management: Knowledge of SQL/NoSQL databases like PostgreSQL, MongoDB.

Cloud Platforms: Familiarity with AWS, GCP, or Azure.

Big Data & Analytics: Understanding of Hadoop, data warehousing, and real-time data processing.

Problem-Solving: Strong analytical skills to optimize data pipelines and troubleshoot issues.

A degree in Computer Science, Data Engineering, or a related field, along with certifications, can be beneficial.

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The role of a Data Engineer is less likely to be fully replaced by Gen AI but will evolve significantly. Instead of replacing Data Engineers, AI will act as a tool to boost efficiency, requiring engineers to adapt by learning AI-driven automation and advanced data engineering techniques.

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my first dream job will be a psychologist and work at nuh hospital. The job responsibilities is to play a vital role in providing comprehensive psychological services to patients. Work in Singapore or overseas both also can. But it would be better if it's in Singapore (nothing related to my course)

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3 years : education and training

5 years : specialization and professional growth

10 years : leadership teaching or high level specialization

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Master’s degree, Bachelor’s degree or doctoral degree license and certification

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not likely as Ai lack of human empathy & emotional connection, ethical & legal challenges, complex & unpredictable human behavior and therapy is more than just conversation

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Data Engineer at ST Engineering or major fintech companies.

Create scalable and efficient data pipelines, databases, and overall data infrastructure.

Optimize database structures, schemas, tables, and queries to improve data storage, retrieval, and analytical processes.

Most likely working in Singapore but if a good opportunity arises, i might work overseas

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1 to 3 years: junior data engineer

4 to 6 years: senior data engineer

7 to 10 years: staff data engineer

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My first dream job would be of a Data Analyst, working behind a desk like a 9 to 5 job. The responsibilities of my job scope would be data cleaning and processing, web-scraping and even conducting statistical analysis. I think it wouldn't matter where I would be located at since I like the idea of working from home.

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Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent work experience.

Strong technical expertise in designing and implementing data pipelines, databases, and analytical tools.

Proficiency in programming languages commonly used in data engineering, such as Python

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1 to 3 years: Entry level job, gain as much knowledge and experience so that my foundation can be strong.

4 to 6 years: Progress to Data Scientist, Machine Learning Engineer, or AI Specialist roles, Shift to remote work or consulting since I prefer more independence.

7 to 10 years: Become a Senior Data Scientist, AI Consultant, or Analytics Lead in a company where I can work mostly solo.

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1. Technical Skills (Hard Skills)

Programming – Python (pandas, NumPy, scikit-learn), SQL (for databases), R (optional)
Data Cleaning & Processing – Strong knowledge of handling messy data
Data Visualization – Power BI, Tableau, Matplotlib, Seaborn
Statistics & Probability – Hypothesis testing, regression analysis
Machine Learning (If AI-Focused) – Scikit-learn, TensorFlow, PyTorch
Automation & Scripting – Using Python to automate data workflows

Big Data (Optional, Future Skill) – Spark, Hadoop for handling large datasets

Cloud Platforms (Optional) – AWS, GCP, or Azure for data storage & analytics

2. Soft Skills (Even for Solo Work)

Critical Thinking & Problem-Solving – Analyzing trends, spotting patterns, and drawing insights
Attention to Detail – Ensuring data accuracy and consistency
Time Management & Self-Discipline – Managing projects independently
Written Communication – Writing clear reports and documentation

3. Qualifications & Certifications

🎓 Degree – A diploma or bachelor’s in Data Science, AI, Computer Science, Business Analytics, or Statistics
📜 Certifications (Helpful but not mandatory):

Google Data Analytics Certification

IBM Data Science or AI Engineering Certification

Microsoft Power BI Data Analyst

AWS Certified Data Analytics (if cloud is relevant)

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My job as a data analyst or AI analyst is unlikely to be fully replaced by Gen AI, but tasks like basic data cleaning, report generation, and simple analysis might be automated—however, roles requiring critical thinking, complex problem-solving, and domain expertise will still need human oversight and decision-making.

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The increased use of Gen AI will not necessarily replace my first job as a data engineer as AI doesn’t design scalable, efficient data architectures. Engineers must optimize storage, pipelines, and processing.
AI-generated solutions may not be optimized for cost and speed in real-world production environments.
AI also lacks the ability to understand nuanced business requirements and troubleshoot complex data issues.