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Average salary for remote Data and Analytics jobs: $95K–$192K across all experience levels.
ICON plc - Contract Research Organizations (CRO) - XLarge
Pragmatike - Staffing & Recruiting - Small startup
US Tech Solutions - Staffing & Recruiting - Large
Jasper & Jute - Retail – Supermarkets & Hypermarkets
American Institutes for Research - Research & Scientific Services - Large
Maxor National Pharmacy Services, LLC - Hospitals & Health Care - Large
Akamai Technologies - Cloud Computing & Infrastructure (IaaS/PaaS) - XLarge
Safelite - Vehicle Repair & Maintenance - XLarge
American Institutes for Research - Research & Scientific Services - Large
Lennar - Real Estate Development & Management - XLarge
Data as a job category has fragmented. The broad data professional of ten years ago, who wrote SQL queries, built dashboards, and occasionally trained models, has been replaced by at least five distinct senior specializations that companies now hire for with different screening criteria, different tooling expectations, and different organizational positioning. For senior professionals with 10 or more years of experience in data-related functions, understanding where their experience sits in this fragmented landscape, and how companies are screening for the specific specialization they need, is the most important thing they can do to improve their remote job market positioning.
The data function in 2026 has five main senior tracks, and companies screen for each of them differently. Data Engineering covers the infrastructure layer: building and maintaining the pipelines, warehouses, and transformation frameworks that make data available to the rest of the organization. Data Analysis and Business Intelligence covers the insight and decision-support layer: analytics managers, BI leads, and the professionals who convert data into business decisions. Data Science covers the modeling and statistical layer: experimentation, causal inference, and predictive modeling. Machine Learning Engineering covers the production ML layer: taking models from development to deployed, monitored, production systems. Analytics Engineering is the newest track, sitting between data engineering and analysis, owning the data modeling layer (typically dbt-based) that makes warehouse data usable for analysts. These tracks do overlap and senior professionals often span more than one, but companies have become significantly more precise about which track they are hiring for, and profiles that do not clearly signal a track are screened out of all of them.
The remote market for senior data professionals is genuinely strong across all five tracks, because data work is inherently tool-mediated, output-evaluated, and asynchronous-compatible. The most favorable supply-demand conditions are in Machine Learning Engineering (production ML experience is genuinely scarce relative to demand), Analytics Engineering (a new enough track that senior practitioners are limited), and Data Science leadership at the Head and VP level (the combination of modeling depth and business leadership is rare). The most competitive tracks are mid-level data analysis and BI, where accessible tooling and training programs have produced a large applicant pool. Senior data professionals who have moved from analysis into function-building, team leadership, or machine learning depth are competing in a materially smaller pool than general data analysts regardless of their experience depth.
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