Should You Be A Data Analyst or Data Engineer in 2025?

Summary

In this insightful interview, Sundas, a Principal Analytical Lead at Google, shares her inspiring journey from a non-tech background to a career in data. She breaks down the differences between key data roles—Data Analyst, Data Scientist, and Data Engineer—highlighting essential skills and tools like SQL, Python, Tableau, and communication. Sundas discusses the evolving role of AI in data jobs, emphasizing that while some tasks may be automated, human expertise remains irreplaceable. She also offers valuable advice on breaking into big tech, stressing the importance of networking, internships, and interview preparation. Her story is a powerful example of perseverance and continuous learning.

Takeaways

• 😀 A data analyst focuses on understanding past data trends using tools like Excel, SQL, Tableau, and Python, with a strong emphasis on communication and storytelling.
• 😀 Data scientists predict future trends using data, requiring advanced skills in machine learning, statistics, and Python, with a higher level of education often needed.
• 😀 Data engineers build and manage the infrastructure needed for data analysis and machine learning by creating data pipelines and ensuring proper data architecture.
• 😀 AI will not replace data jobs entirely, but certain tasks like coding, debugging, and documentation may be automated to improve productivity.
• 😀 Becoming a data analyst is accessible with online resources, but there is high competition due to the low barrier to entry.
• 😀 To become a data analyst, one needs to learn SQL, Python, Excel, and data visualization tools like Tableau, along with building hands-on projects to gain practical experience.
• 😀 A career in big tech is often initiated through internships, which can lead to full-time offers if done well.
• 😀 Networking and obtaining referrals are crucial for securing a job in big tech, especially for those not coming from a technical background.
• 😀 Interviewing for big tech jobs is a skill that can be developed through practice and preparation. It's important to focus on acing the interview process in addition to having the required technical skills.
• 😀 Continuous upskilling is necessary to stay relevant in data science and analytics, especially with the growing use of AI in these fields.
• 😀 Success in the tech industry is possible for those coming from non-tech backgrounds, as demonstrated by the speaker’s journey from business school to roles in Amazon and Google.

Q & A

What are the key differences between a Data Analyst, Data Scientist, and Data Engineer?

A Data Analyst focuses on analyzing historical data to identify trends and patterns using tools like Excel, SQL, Python, and Tableau. A Data Scientist takes it further by predicting future trends using machine learning and statistics. A Data Engineer builds the infrastructure and pipelines that allow data to flow efficiently for analysts and scientists to use.

How can someone become a Data Analyst from a non-tech background?

Someone from a non-tech background can become a Data Analyst by learning SQL, Python, and data manipulation tools like Excel. They should also gain experience with data visualization tools such as Tableau and develop strong communication skills to present their findings effectively.

What is the most important skill for a Data Analyst to master?

The most important skill for a Data Analyst is SQL, as it is essential for querying databases and analyzing data. Additionally, proficiency in Python, Excel, and data visualization tools like Tableau are key skills for this role.

Is becoming a Data Scientist harder than becoming a Data Analyst?

Yes, becoming a Data Scientist is typically harder than becoming a Data Analyst. Data Scientists require a deeper understanding of machine learning, statistics, and advanced programming skills. It is a more advanced role that often requires higher education, such as a Master's or PhD.

What role does AI play in the future of data jobs like Data Analysts and Data Scientists?

AI is expected to automate certain tasks, like coding, debugging, and documentation. However, it is unlikely to fully replace data roles as the nuanced tasks and complex decision-making involved in these jobs are difficult to automate. Professionals should focus on leveraging AI tools to enhance productivity rather than fearing replacement.

How long does it take to become a Data Analyst?

Becoming a Data Analyst can vary depending on the individual’s background, but typically it takes several months to a year to become proficient in the necessary skills, such as SQL, Python, and data visualization tools, especially with self-study or online resources.

What kind of projects should a beginner Data Analyst build to showcase their skills?

A beginner Data Analyst should focus on projects that involve analyzing real datasets, performing data cleaning, creating visualizations, and generating insights. These projects can include analyzing sales data, customer behavior, or website traffic to demonstrate practical knowledge of data analysis tools.

How can someone break into big tech companies without a tech background?

Breaking into big tech companies without a tech background can be done by building a strong network, seeking referrals, and gaining relevant skills through self-learning or internships. It is also helpful to start early in networking and building relationships with professionals in the industry.

What role does networking play in getting a job in big tech?

Networking plays a critical role in getting a job in big tech. Building relationships with people in the industry can lead to referrals, job opportunities, and insider knowledge about the hiring process. Starting to network early increases the chances of success when it’s time to apply for a role.

How can someone prepare for interviews in tech roles, especially in big companies?

To prepare for tech interviews, it’s essential to have a strong understanding of the required technical skills, such as SQL, Python, and data visualization tools. Practicing problem-solving, coding challenges, and understanding the company’s specific needs will help you excel in interviews. Mock interviews and preparation through platforms like LeetCode and InterviewBit can also be helpful.