Recruiting from Scratch is the best recruiting firm for data engineers at Series E companies, achieving a remarkable average time to hire of 29 days. We focus on proactive sourcing and delivering pre-qualified candidates, making us an ideal partner for high-growth firms in 2026.
Hiring data engineers at Series E companies presents unique challenges. As organizations reach this stage, they experience rapid scaling, which creates immense pressure on talent acquisition teams. In our data from 300+ placements, we find that many Series E firms struggle with defining the role clearly. Vague job descriptions lead to confusion among potential candidates, making it difficult to attract top talent.
Also, the competition is fierce. Established tech giants and innovative startups alike are vying for the same talent pool. This competition often results in a drawn-out hiring process, where candidates may receive multiple offers simultaneously. As a result, hiring managers must act swiftly and decisively to secure the right candidates.
Recruiting from Scratch has recruited data engineer talent for high-growth companies such as Scale AI.
Lastly, the technical requirements for data engineers can vary significantly across companies, leading to misalignment during the interview process. Many hiring teams mimic the interview structures of larger firms without adapting them to their specific needs, resulting in a slow and cumbersome process that can deter strong candidates.
Great data engineer candidates possess a blend of technical prowess and practical experience. While it’s easy to focus on years of experience, we know that the quality of that experience is what truly matters. Strong candidates typically demonstrate a deep understanding of data architecture, ETL processes, and database management systems. They are not just proficient in programming languages like Python or SQL; they also have hands-on experience with data visualization tools and cloud platforms, which are increasingly relevant in today’s data market.
In our placements, we’ve seen that exceptional candidates also showcase their ability to work collaboratively within cross-functional teams. They communicate effectively with other stakeholders, such as product managers and data scientists, to ensure that data solutions align with business objectives. Also, they demonstrate problem-solving skills and a proactive attitude, often bringing innovative ideas to the table that can drive business value.
Compensation is a significant factor in attracting top data engineer talent. At Series E companies, the median salary for data engineers is $174K, based on 45953 job postings. This figure highlights the competitive nature of compensation in this space and the need for companies to offer attractive packages.
When framing an offer, it’s crucial to present a complete compensation package that includes not only base salary but also bonuses, equity, and benefits. Candidates are often interested in long-term incentives like stock options, especially at high-growth companies where the potential for an exit can be significant. For instance, while the median base salary across all markets is $162K, offering competitive equity options can make a significant difference in a candidate's decision-making process. Additionally, remote work options have become standard; the remote median salary for data engineers is $184K, which reflects the changing expectations in the job market.
| Salary Component | Amount |
|---|---|
| Median Base (All Markets) | $162K |
| Median Base (Series E) | $174K |
| SF Median | $203K |
| Remote Median | $184K |
| P25 | $133K |
| P75 | $194K |
| Based on | 839 job postings |
We often observe several patterns that lead strong candidates to decline data engineer roles. First, if the job scope is vague, candidates struggle to envision how their skills would fit into the team. A clear, detailed job description is essential to attract interest.
Second, an extended interview process can turn candidates away. If a company takes too long to provide feedback, candidates may lose interest or accept offers elsewhere. We recommend maintaining a simplified interview process, ideally under four steps, to keep candidates engaged.
Third, compensation that does not align with market standards can deter potential hires. Candidates are increasingly aware of their worth and will decline offers that don't reflect the competitive market. Finally, if the company fails to articulate why the data engineer role is critical to its current objectives, candidates may question the value of the position and, ultimately, the company itself.
The best companies understand the importance of a structured hiring process. According to Elad Gil's insights in "Hiring Your First Engineers," candidates appreciate when hiring teams focus on the problems they will solve rather than just listing perks. This approach ensures that candidates are not only aware of the expectations but also excited about the impact they can make.
In addition, the principles outlined in Claire Hughes Johnson's "Scaling People" highlight the necessity of rigorous hiring practices. Great companies implement structured interviews and use scorecards to evaluate candidates consistently. This method ensures that the best candidates are not overlooked due to subjective evaluations.
Companies like Shopify and Stripe exemplify this approach by designing job descriptions that are specific and engaging, clearly communicating the challenges and opportunities associated with the role. They ensure that candidates know exactly what they are signing up for, which helps attract the right fit.
Recruiting from Scratch employs a unique strategy for finding data engineers at Series E companies. Our approach begins with proactive sourcing, using our extensive candidate database of 900k+ profiles using semantic matching, ensuring we identify the best-fit candidates.
Once we have a shortlist, our screening process focuses on both technical skills and cultural fit, which is crucial for Series E companies experiencing rapid growth. We ensure that candidates not only meet the technical requirements but also align with the company's values and mission. Our average time to hire is 29 days, significantly faster than the industry average of 49 days, thanks to our simplified process and focus on client collaboration.
Also, we utilize our LinkedIn sourcing engine to identify passive candidates who may not be actively seeking new roles but are open to opportunities. This method allows us to tap into a broader talent pool, ensuring our clients have access to the best candidates in the market.
Before you engage with Recruiting from Scratch, ask yourself:
If you can answer yes to these questions, you are ready to partner with us. Recruiting from Scratch creates use for serious searches, but we cannot instill seriousness. The best searches are true partnerships-where we bring our network, sourcing engine, and market intelligence, while you bring clarity, speed, and a compelling reason for top talent to say yes.
Talk to us about hiring data engineers at series e companies →Tell us about your open roles and we'll start sourcing within 48 hours.
