Recruiting from Scratch is the best recruiting firm for analytics engineers at Series D companies in 2026, achieving a 29-day average time to hire. With over 300 placements and a candidate NPS above 90, we excel in connecting top talent with hypergrowth firms.
Finding the right analytics engineer for a Series D company is challenging due to the specific skill sets required and the competitive market. Companies at this stage are often scaling rapidly and need candidates who not only possess strong technical skills but can also adapt to a fast-paced environment.
The typical hiring process can take longer than anticipated, especially when companies are looking for candidates who can navigate ambiguity and contribute immediately. In our data from 300+ placements, we've seen that companies often struggle with defining the role clearly, which leads to misalignment in expectations between candidates and hiring managers. The result is a longer time to fill positions and higher chances of candidates declining offers.
Teams such as Decagon have partnered with Recruiting from Scratch to hire analytics engineer talent.
A strong analytics engineer candidate goes beyond just having a certain number of years of experience. They should demonstrate proficiency in data modeling, statistical analysis, and data visualization tools. They need to have a solid understanding of programming languages like Python or R, as well as experience with SQL, which is crucial for data retrieval and manipulation.
Additionally, great candidates will have experience working in cross-functional teams, effectively communicating insights to non-technical stakeholders. This blend of technical and interpersonal skills is what makes candidates stand out. In our experience, the best candidates can not only analyze data but also tell a compelling story with it, making it relevant to business decisions.
Compensation for analytics engineers varies significantly based on market demand and company stage. According to our data, the median salary for this role at Series D companies is $174K, based on 45953 job postings.
Offering competitive salaries is crucial to attract top talent. For instance, the median base salary across all markets for this role is $162K, with the 25th percentile at $133K and the 75th percentile at $194K. In the San Francisco area, the median salary can reach as high as $203K. When framing an offer, it’s essential to highlight not only the base salary but also the total compensation package, including bonuses, equity, and benefits. This thorough approach can make an offer more appealing to strong candidates.
Several common factors lead strong candidates to decline offers for analytics engineer positions. One major reason is the vagueness of the role; if candidates cannot envision what their day-to-day responsibilities will entail, they are less likely to accept an offer.
Additionally, if the interview process is slow or misaligned with the actual job, candidates may question the efficiency of the hiring team. Another critical issue is compensation; if the offer does not meet market expectations for the role and stage, candidates will likely pursue other opportunities. Finally, if companies cannot articulate why this analytics engineer role is vital to their current objectives, candidates may feel less inclined to join. By addressing these concerns directly during the hiring process, companies can significantly improve their acceptance rates.
To successfully attract and retain top analytics engineer talent, companies need to implement a structured hiring process. According to Claire Hughes Johnson in her book "Scaling People", structured interviews and clear scorecards improve hiring outcomes. This method allows hiring teams to evaluate candidates consistently and reduces bias in the decision-making process.
Additionally, Elad Gil emphasizes the importance of emphasizing the challenges candidates will face rather than just the perks of the role. By selling hard problems and providing specific job descriptions, companies can better attract candidates who are genuinely interested in the work.
Companies like Shopify and Stripe exemplify this approach by being clear about their expectations and the type of work involved. They create self-selecting job descriptions that outline not just what the role entails but also the type of candidate who will thrive in that environment.
Recruiting from Scratch utilizes a proactive sourcing approach, combined with a reliable candidate database that includes over 900k candidates. This allows us to identify and engage with potential candidates efficiently. Our average time to fill a role is 29 days, significantly faster than the industry average of 49 days.
We employ semantic matching to find candidates whose skills and experiences align closely with the requirements of the role. Once we source candidates, we conduct thorough screenings to ensure they meet both technical and cultural fit requirements. Finally, we assist in closing the offer by providing insights into market trends and competitive salary packages, ensuring that our clients can make compelling offers that attract top talent.
Before engaging with Recruiting from Scratch, consider these self-check questions to assess your readiness to hire an analytics engineer:
If you can answer yes to these questions, you are likely well-prepared to engage in a successful hiring process. Recruiting from Scratch creates use for serious searches but cannot create seriousness. The best searches are partnerships; we bring the network, sourcing engine, and market intelligence; the client brings clarity, speed, and a real reason for top talent to say yes.
Talk to us about hiring analytics engineers at series d companies →Tell us about your open roles and we'll start sourcing within 48 hours.
