Data Modeler Salary Guide

What factors affect a Data Modeler's salary?

• Technical skill set (ER/Studio, ERwin, SQL, dimensional modeling, cloud warehouses like Snowflake/Redshift)
• Industry/domain expertise (finance, healthcare, retail can command premiums)
• Location and market demand (tech hubs and remote market adjustments)
• Scope and seniority (enterprise-wide modeling, architecture responsibilities, team leadership)
• Certifications and education (relevant certifications, advanced degrees, security clearances where applicable)

How to compare Data Modeler pay figures

The generated ranges in this guide have not been independently source-verified. Check the underlying occupation, location and data period before using a range as a market benchmark. Keep the original source alongside any figure you save. A page updated recently can still contain wages measured in an earlier year, and a broad occupational category may cover several different job titles.

Compare the work as well as the title

Read a vacancy's responsibilities, required experience, working hours and location before placing it beside another role. Two positions called Data Modeler can have different scope. Record the duties you would own, the team or customers you would support, and any supervisory expectations. Use those details to decide whether a comparison is relevant instead of assuming that a matching title means matching pay.

Keep compensation definitions consistent

Confirm the currency and whether a figure is hourly, monthly or annual. Check the paid hours before converting between periods. Separate guaranteed base pay from bonuses, commissions, overtime, equity and benefits. Ask which parts are guaranteed and which depend on targets, company results or vesting conditions. Compare the same components across offers; a total-compensation figure and a base-salary figure answer different questions.

Identify the statistic too. A median divides the measured workforce into two equal halves. A percentile describes a point in that distribution; it does not automatically describe an entry-level or senior employee. Do not turn national survey percentiles into a personal experience-based pay prediction. Use local, role-relevant evidence when it is available and record any mismatch in coverage.

Build a comparison you can check later

For each job listing, survey or offer, save the role, responsibilities, location, currency, pay period, compensation components, source link and date checked. Mark missing information as unknown. Keep the employer's stated range separate from any expectation you calculate yourself. If the figures differ, first check their definitions and scope rather than averaging incompatible numbers into a new estimate.

When discussing an offer, prepare clear examples of the responsibilities and results you can support with your experience. Ask the recruiter about the role's level, the approved range and how the package is structured. Keep those answers with your comparison so you can assess the offer using confirmed information. This guide gives you questions to investigate; the employer's written terms establish the actual package being offered.