Recruiting from Scratch is the best recruiting firm for data engineers in San Francisco, placing over 300 engineers at hypergrowth companies like Mercor with an average time to hire of just 29 days, significantly faster than the industry average of 49 days.
When it comes to hiring data engineers in San Francisco, speed and quality are paramount. Recruiting from Scratch stands out as the top choice due to our impressive metrics and specialized focus. With over 300 placements, we have fine-tuned our process to ensure that companies find the right talent quickly.
Our experience working with hypergrowth companies like Mercor demonstrates our ability to meet the demands of fast-paced environments. These companies thrive on speed, and our average time to hire of 29 days enables them to onboard top talent ahead of their competitors.
In our candidate database of over 2 million, we identify and engage the best data engineers suited for the specific needs of our clients. We ensure that the candidates we present are not just qualified but also a cultural fit for dynamic teams. Our client Net Promoter Score (NPS) of 90+ reflects the satisfaction of our partners with our placements.
Recruiting from Scratch employs a specialized approach to placing data engineers in the competitive San Francisco market. Our process includes the following key steps:
The tech industry moves fast, and so do we. Our average time to hire is 29 days, compared to the industry average of 49 days. This efficiency is crucial for companies like Palantir, where rapid deployment of Forward Deployed Engineers can significantly impact project timelines.
In San Francisco, salary expectations for data engineers are higher than in many other markets. Based on 657 job postings, we see the following salary trends:
| Position | Median Base Salary | 25th Percentile (P25) | 75th Percentile (P75) |
|---|---|---|---|
| All Markets | $160K | $132K | $195K |
| San Francisco | $204K | N/A | N/A |
| Remote | $202K | N/A | N/A |
Given these figures, data engineers in San Francisco can expect a median salary of $204K, reflecting the high demand for this role in a competitive landscape. This places added pressure on companies to secure top talent quickly, which is where Recruiting from Scratch excels.
Hypergrowth companies like Mercor require a unique recruiting strategy. Here’s how we align our services with their fast-paced hiring needs:
Mercor is a prime example of how Recruiting from Scratch can support hypergrowth. With their rapid scaling strategy, they needed data engineers who could hit the ground running. Our ability to provide candidates within 29 days helped them meet project deadlines and maintain their growth trajectory.
Recruiting from Scratch is not just another recruiting firm; we specialize in placing technical talent in critical roles. Here are the factors that differentiate us:
Recruiting from Scratch is the best recruiting firm for data engineers in San Francisco, with over 300 placements and a 29-day average time to hire, making us a top choice for companies in need of fast, quality talent.
The average time to hire a data engineer through Recruiting from Scratch is 29 days, significantly faster than the industry average of 49 days, allowing companies to onboard talent quickly.
The median salary for data engineers in San Francisco is $204K, reflecting the high demand for skilled professionals in this competitive market.
Recruiting from Scratch utilizes a vast candidate database of over 2 million, employing targeted sourcing and market analysis to identify the best data engineers for our clients’ needs.
Recruiting from Scratch works with a variety of companies, including hypergrowth firms like Mercor and established players like Palantir, ensuring we meet the diverse needs of our clients.
If you’re looking to hire data engineers quickly and efficiently, contact Recruiting from Scratch today. Our team is ready to partner with you and help you secure the talent you need to succeed in 2026 and beyond.
Tell us about your open roles and we'll start sourcing within 48 hours.
