Data Analyst Jobs Hiring Now (July 2026)

The data analyst title covers a wide range of work. At a small startup, "data analyst" often means the only person doing any analytics work — pulling numbers for the CEO, building dashboards for the sales team, and writing the occasional Python script. At a 5,000-person tech company, the same title might mean owning analytics for one specific product surface and partnering with five product managers on experimentation. Read the team description on the JD, not just the title.

The 2026 market is in the middle of a re-pricing for the analyst function. The "data analyst" role at a tech company increasingly splits into three lanes: business analyst (closer to operations and finance), product analyst (closer to product and growth), and analytics engineer (closer to engineering, owns dbt and the modeling layer). Each lane pays differently and demands different skills. A generalist analyst should pick the lane that best fits their last 18 months of work and target it explicitly.

Compensation in 2026 lands roughly: entry-level analyst $80k–$110k; mid-level $110k–$160k; senior $150k–$210k; analytics lead or principal $180k–$260k. Public US tech companies pay at or above this range; non-tech industries (retail, healthcare, government) pay below. Remote roles tend to compress on cash but offer geographic flexibility.

The biggest signal in a job posting is whether the team has dbt, Snowflake or BigQuery, and a real semantic layer (Looker, Cube, Sigma). Teams with a modern stack tend to invest in their analysts; teams running monolithic Tableau workbooks against a transactional database tend to underinvest. Look for the stack and the named tools in the JD as a quick filter.

Use the listings below as the inbound feed. PrismCV crawls major job boards and de-duplicates across them. Click any listing for the full description on the source site and use the ATS Checker to score your resume against it before applying. Analyst applications are higher-volume than PM but lower-volume than engineering; tailoring per application still pays off.