62.1K
THData Engineer vs AI Engineer.
Here’s what each role does, what they earn, and how to choose.
What You Actually Do:
Data Engineer: Pipelines and reliability. Ingest, transform, model, validate. If data breaks, everything downstream breaks. Building data foundations that analytics, ML, and product teams rely on.
AI Engineer: Models in production. RAG systems, agent evaluations. If the model is slow, wrong, or unsafe, you fix it. Building AI features like chat, search, copilot, automations that users actually touch.
Languages You Use:
Data Engineer: SQL all day, Python for pipelines, Scala or Java for Spark.
AI Engineer: Python for model workflows, TypeScript or JavaScript for APIs, some SQL.
Tech Stack:
Data Engineer: Snowflake, BigQuery, Redshift, dbt, Airflow, Kafka, Databricks, Spark, Monte Carlo.
AI Engineer: OpenAI, Anthropic, Gemini, LangChain, LangGraph, Pinecone, Weaviate, Fireworks AI, Ragas, LangSmith, Weights & Biases.
Salary Ranges (NYC/SF):
Data Engineer: $140K-$200K base, $170K-$240K total comp
AI Engineer: $160K-$230K base, $200K-$300K total comp (higher at AI-first companies with equity)
Interested in data and building scalable systems? Data engineering.
Like AI and want to work with models in production? AI engineering.
@the.datascience.gal
