Data Scientist vs Analytics Engineer: Salary & Career Benchmarks in the US

In the US, Data Scientist roles typically command a higher median salary than Analytics Engineer positions. According to our 2026 live benchmark data, a mid-level Data Scientist earns a median salary of $160K, whereas a Analytics Engineer brings in roughly $136K (a gap of $24K at the median).

Seniority, tech stack, and location all move this gap. Senior practitioners in either discipline can exceed the upper range through specialist skills. See the skills premium section below for the specific certifications and tools that push offers to the top of the range.

While both positions are vital to a modern tech organisation, Data Scientist and Analytics Engineer have fundamentally different daily workflows.

Data Scientist focuses primarily on building statistical models, running predictive analysis, and translating data into business decisions. Day-to-day work revolves around training machine learning models, querying data warehouses, performing exploratory analysis in Jupyter notebooks, and presenting insights to stakeholders.

Analytics Engineer focuses on transforming raw data into trusted, business-ready datasets using modelling layers and semantic logic. Their time is spent writing and testing dbt models, maintaining data catalogues, collaborating with data analysts on metric definitions, and ensuring data quality across the warehouse.

Transitioning between these two paths is achievable but requires targeted upskilling.

Moving from Data Scientist to Analytics Engineer: Data analysts with strong SQL and dbt skills are the most natural fit. The role sits at the intersection of engineering and analysis, so both paths transition in comfortably.

Moving from Analytics Engineer to Data Scientist: A strong statistics or mathematics background is the most common entry point. Software engineers with ML exposure transition in quickly. The harder gap to bridge is business communication: turning model outputs into decision-ready narratives.

Neither path requires starting from scratch. Professionals in both roles share underlying technology fluency; the gap is usually domain knowledge and specific tooling rather than core engineering fundamentals.

In the US in 2026, both roles are seeing demand, but with different drivers.

Data Scientist demand is high, particularly in AI and data platform teams across New York, Austin, and San Francisco Bay Area, led by employers such as OpenAI and Anthropic. Analytics Engineer demand is very high, concentrated in AI and data platform teams across Seattle, San Francisco Bay Area, and Austin, led by employers such as Scale AI and OpenAI.

Workspace flexibility significantly impacts total compensation value in the US.

Data Scientist roles score 72% on our remote-friendliness index (High). This is because much of the work is asynchronous and tool-driven, though many US employers, particularly larger firms with formal return-to-office mandates, still expect two to three days a week in a New York or Austin office. Where in-office attendance is required, it is typically driven by cross-functional collaboration and stakeholder alignment sessions, which US employers headquartered in New York and Austin increasingly formalize into a fixed hybrid schedule rather than leaving to team discretion.

Analytics Engineer roles score 82% (Very High). Much of the work is asynchronous and tool-driven, though many US employers, particularly larger firms with formal return-to-office mandates, still expect two to three days a week in a Seattle or San Francisco Bay Area office is the primary driver of flexibility. When office days are required, it is usually for cross-functional collaboration and stakeholder alignment sessions, which US employers headquartered in Seattle and San Francisco Bay Area increasingly formalize into a fixed hybrid schedule rather than leaving to team discretion.