Data Analyst vs. Data Scientist vs. Data Engineer
All three work with data and all three list SQL and Python, so resumes look alike on keywords. The jobs are different: analysts answer business questions with reports and dashboards, data scientists build statistical and machine-learning models, and data engineers build the pipelines that get the data into place for the other two. Analytics engineers sit between analyst and engineer, modelling warehouse tables (usually in dbt).
How to tell them apart on a resume
Data Analyst
Tableau, Power BI, Looker, Excel, dashboards, KPIs, stakeholder reporting. Output is a report or a recommendation.
Data Scientist
Scikit-learn, PyTorch, experiments, A/B tests, forecasting, model accuracy. Output is a model or a statistical finding.
Data Engineer
Airflow, Spark, Kafka, dbt, Snowflake, pipelines, data volumes, uptime. Output is reliable data that others use.
The question that settles it
“When you finish a piece of work, what do you hand over: a dashboard or answer, a model, or a pipeline that other people depend on?”
Read the full definitions
Open the full tool for the other look-alike pairs, role profiles, and the JD decoder.