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Choosing Between Data, ML, and Software Careers

Author

DEEP CHATTERJEE
August 27, 2025

Starting a career in tech or switching paths can be confusing.
The job titles sound similar. The work is not.

Pick the wrong one, and you may spend months learning skills you never use.
Or worse, land in a job that doesn’t suit you.

Here’s a simple breakdown of five common roles.

🕵️ Data Analyst

  • What they do: Answer business questions with numbers.

  • Tools: SQL, Excel, Tableau/Power BI.

  • Pay (US median): ~$106k.

  • Good if you like: Finding patterns, telling stories with data.

  • Things to note: Lots of ad-hoc requests. Work can feel repetitive.

🏗️ Data Engineer

  • What they do: Build systems that move and clean data.

  • Tools: Python, Spark, Airflow, cloud platforms.

  • Pay: ~$155k.

  • Good if you like: Solving technical puzzles.

  • Things to note: On-call duties. Often behind the scenes.

🔬 Data Scientist

  • What they do: Use stats and ML to make predictions.

  • Tools: Python/R, SQL, stats, ML frameworks.

  • Pay: ~$168k.

  • Good if you like: Ambiguity and problem-solving.

  • Things to note: Higher entry barrier. Career path can be unclear.

🤖 Machine Learning Engineer

  • What they do: Take ML models into production.

  • Tools: Python, PyTorch, Hugging Face, system design.

  • Pay: ~$250k.

  • Good if you like: Clear impact and technical puzzles.

  • Things to note: Field moves fast. Stressful when systems fail.

💻 Software Engineer

  • What they do: Build the apps and systems people use daily.

  • Tools: Programming languages, algorithms, cloud, CI/CD.

  • Pay: ~$182k.

  • Good if you like: Building concrete solutions.

  • Things to note: On-call work. Need to keep learning new tools.

How to Decide

Ask yourself:

  • Do you prefer building systems or discovering insights?

  • Do you like clear tasks or open-ended problems?

  • How fast do you want to get started?

Remember: switching paths is common.
Analysts become Data Scientists.
Software Engineers move into Data Engineering.
Data Scientists shift into ML Engineering.

Your career is not fixed.
Pick the path that matches your strengths today.

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