allplants: scaling their product, data and engineering teams post Series B
Curious to hear how Confido helped a mission-driven B Corp like allplants to double in size?
Here’s how we helped allplants navigate that tricky transition from one small engineering team, to a scalable, multi-team structure.
What is allplants?
allplants was created to make eating more plants exciting and easy, delivering delightful, healthier food to busy homes everywhere.
The science is irrefutable. Eating more plants is the #1 way to help yourself and our planet thrive.
That’s why the allplants team have started by helping this rapidly emergent plant curious consumer, by making a step into plants a supremely positive choice: a fiesta, not a fast - to accelerate the world's transition to eating more plants.
What did allplants need?
In October 2021, allplants secured a whopping £38mn Series B investment. This funding was to be used for expansion into new markets, building out a new kitchen facility, hiring new talent and expanding their offerings.
A key part of this growth was building a product, data and engineering function that could deliver on these growth plans. Back then, product and engineering were all one team. So, they needed that team to split out into a more scalable structure.
They decided on a squad model. That way they could have self-contained product and engineering teams, aligned to areas of the business with their own product roadmap. They also wanted to set up a data engineering hub to provide better insights and reporting across the business.
What hires did we need to make?
To get this plan into motion, we needed to make the following product, data and engineering hires. This involved working with individual stakeholders, plus allplants' Head of Talent.
Our focus throughout this process was balancing consistency, quality and responsiveness to ensure an excellent candidate and stakeholder experience.
Product hiring challenge:
- allplants had been trying to hire both Product Manager roles directly for over two months with little success.
- We interviewed the stakeholders to understand the scope of the roles and share our experience from helping other startups to hire.
- We agreed these roles were at the wrong salary and seniority bandings, the Head of Product got them approved at Lead level.
- We created a specific sourcing strategy for each Lead Product Manager:
- Growth Team – mapping ecommerce companies in the UK, focusing on the PM’s with experience in acquisition and conversion.
- Platform Team – headhunting PM’s that had experience working on internal platforms, infrastructure, and systems.
- The result: 4 week average time to hire and 3 candidate average per hire, across both roles.
Head of Data hiring challenge:
- This was a brand new role for BOTH the business and the main stakeholder (Finance Director).
- We spent time understanding the business need, current team composition and key responsibilities of this hire.
- We advised the stakeholder and Head of Talent that there were two types of Head of Data we could hire:
- Type 1: A “stepping up into the role” - a data lead that wanted to take on their first Head of Data role.
- Type 2: An experienced Head of Data - someone who had held this role at least once before.
- We went to market open to both scenarios, interviewing 3 candidates in the first 4 weeks.
- The result: By week 5, the stakeholder agreed Type 1 was best suited for the business. By week 8 the hire was made.
If you need help with your product or engineering hires, or are looking for a new role at a tech for good startup, drop us an email at email@example.com.
If you want to work at allplants but don't work in tech or product, check out their other live roles at Jobs For Good.