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Data Engineer vs Data Scientist vs ML Engineer

Three roles around the model pipeline. Data engineers deliver trustworthy data; data scientists design and train the models; ML engineers put those models into production and keep them running.

At a glance

Data EngineerData ScientistML Engineer
Salary comparison$110 000 – $150 000$110 000 – $145 000$120 000 – $160 000
Training Duration6–18 months9–24 months9–24 months
Job Search Duration3–9 months4–12 months4–10 months
English LevelB1–B2 — for reading cloud docs and working with international data teamsB2 — for reading research papers and working with international teamsB2 — for reading research papers and technical documentation
EducationBachelor's in CS or STEM is common — a strong portfolio compensates for a missing degreeBachelor's in STEM is typical — a strong portfolio compensates for a missing degreeTechnical degree with strong math background preferred — the math foundation is hard to build alone
Demand TrendHigh DemandHigh DemandHigh Demand

Salary comparison

Data Engineer

United States
Junior$80 000 – $110 000
Middle$110 000 – $150 000
Senior$155 000 – $200 000

Source: Habr Career, Glassdoor 2025

Data Scientist

United States
Junior$80 000 – $105 000
Middle$110 000 – $145 000
Senior$145 000 – $190 000

Source: Habr Career, Glassdoor 2025

ML Engineer

United States
Junior$90 000 – $120 000
Middle$120 000 – $160 000
Senior$160 000 – $220 000

Source: Habr Career, Glassdoor 2025

Skills compared

Data Engineer

Technical Skills

Advanced SQL & Data ModelingPython (PySpark, pandas)ETL/ELT Pipelines (Airflow, dbt)Data Warehousing (Snowflake, BigQuery, Redshift)Big Data (Apache Spark, Kafka)Pipeline OrchestrationCloud Platforms (AWS, GCP, Azure)Databases (PostgreSQL, ClickHouse, NoSQL)Data Quality & TestingGit, CI/CD, Infrastructure as Code

Soft Skills

Problem-SolvingStakeholder CommunicationAttention to DetailSystems Thinking

Data Scientist

Technical Skills

Python, Pandas, NumPyStatistics & ProbabilitySQL & Database QueryingMachine Learning (Scikit-learn)Data Visualization (Matplotlib, Plotly)Data Wrangling & ExplorationDeep Learning (PyTorch, TensorFlow)Feature EngineeringA/B Testing & Experiment DesignBig Data (Spark, Cloud Pipelines)

Soft Skills

Critical ThinkingStakeholder CommunicationBusiness Domain KnowledgeCuriosity & Deep-Dive Analysis

ML Engineer

Technical Skills

Python for ML (NumPy, Pandas)ML Frameworks (PyTorch, scikit-learn)Deep Learning (Transformers, CNNs)Linear Algebra, Calculus, StatisticsData Processing & Feature EngineeringModel Deployment (MLflow, TorchServe)SQL for Data AccessDocker & ContainerizationGit & MLOps Practices

Soft Skills

Problem Formulation & DecompositionResearch Paper Reading & ImplementationTechnical Communication

Key differences

  • Data engineers build the data platform — pipelines, warehouse, quality — so the other two work on fresh, reliable inputs.
  • Data scientists work upstream of production: statistics, experimentation, model design, notebooks. They answer "which model and why?"
  • ML engineers work at the production boundary: serving, scaling, monitoring, MLOps. They answer "how do we run this model reliably for real users?"
  • Python and SQL are shared; the split is data platform (engineer) vs model design (scientist) vs model operations (ML engineer).
  • The three form a chain: no clean data, no good model; no good model, nothing to serve; no reliable serving, no business value.

If you like infrastructure and reliability, be a data engineer. If you like research, statistics, and model design, be a data scientist. If you like turning a model into a reliable production system, be an ML engineer.

Compare two at a time

Data Engineer vs Data ScientistData Engineer vs ML EngineerData Scientist vs ML Engineer

Go deeper

Data Engineer

Build the pipelines that turn raw data into reliable analytics. Data engineers design warehouses, automate ETL/ELT flows, and make data trustworthy for analysts and scientists.

Data Engineer roadmapData Engineer salary guide

Data Scientist

Turn raw data into decisions that move the business forward. Data scientists combine statistics, programming, and domain expertise to find patterns others miss.

Data Scientist roadmapData Scientist salary guide

ML Engineer

Machine learning engineers build the AI systems that power recommendations, search, autonomous vehicles, and language models. It is one of the highest-paid and fastest-growing roles in technology.

ML Engineer roadmapML Engineer salary guide

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