Data Engineering at Salesforce

The trade at Salesforce is stability and brand in exchange for pay that sits below other Technology companies. Glassdoor ratings land among the highest of the companies we track, which reflects solid baseline satisfaction, but anonymous forum sentiment skews more critical and internal happiness is trending down. That gap tells you something: engineers who value process maturity, organizational scale, and the Salesforce name on their resume tend to report decent experiences; those who arrived expecting FAANG-level comp and fast-moving teams find the friction harder to absorb. At this headcount, data platform teams often sit several layers from the product roadmap, and data architecture decisions can involve longer approval cycles than engineers coming from startups or smaller orgs expect. Comp won't clear the bar against peers; if stability and brand are the goal, the math may still work out.

Since early 2023, Salesforce has been working through a significant restructuring: it cut roughly 10% of its workforce under pressure from activist investors, and has since had 3 tracked layoffs in the past 12 months, the most recent in May 2026. The 30 day layoff signal is currently low, suggesting the most acute phase has passed. The 24 open data engineering roles across 11 cities, concentrated in San Francisco and a handful of regional hubs, indicates continued investment in data infrastructure, particularly around AI-adjacent pipelines as the company pushes Einstein and Agentforce deeper into its product suite. For someone joining now, the picture is stabilization rather than expansion: data headcount is moving but the pace reflects a company managing margins as much as growing them. Engineers who join in the next 12 months will likely spend as much time consolidating existing pipelines as building new ones.

Salesforce runs one of the largest multi-tenant SaaS platforms in enterprise software, which means data engineering there centers on scale and isolation: hundreds of thousands of org tenants generating transaction and event streams across Sales Cloud, Service Cloud, and a growing suite of AI-powered features. The core data problem is routing, aggregating, and serving that customer data while respecting strict tenant boundaries and an expanding regulatory surface across GDPR, CCPA, and financial compliance verticals. The stack, Airflow, AWS and EMR with Python, SQL and Scala, maps to orchestrated batch pipelines and distributed compute; Scala's presence signals that some paths carry enough volume to justify it beyond Python's reach. For a data engineer, the day-to-day tilts toward warehouse and ingestion work that supports both the product's internal analytics and Einstein AI features, where data quality and SLA consistency are non-negotiable.