How Fintech Start-Up Engineering Leaders Manage Rapid Scaling

Recently, we hosted a Lohika Re:think online event with our friends from Capgemini AIE. The Applied Innovation Exchange (AIE) is Capgemini’s global platform that leverages a framework for action, a network of exchange locations, and a high-performance engagement experience.

The event featured a panel of fintech engineering leaders who discussed how they scaled their engineering teams to take advantage of market opportunities. The panel:

• Moderator: Yashwanth Hemaraj, Partner at Benhamou Global Ventures (BGV)
• Kevin Doerr, Chief Product Officer at Marqeta
• Shane O’Flynn, Head of Engineering at TradeIX
• Jeff Winner, CTO at Happy Money

In this post, I’ll share some of the insights shared by the panel.

The definition of “rapid scaling”

For Shane O’Flynn, rapid scaling is not an engineering problem, it’s a people problem. According to Shane, any technological challenge, no matter how complex, can be solved by putting a team of brilliant engineers to work on it. The bigger challenge is the people aspect and Shane believes that soft skills are essential in engineering leadership. 

At TradeIX, Shane is looking at doubling the engineering team this year, after doubling its size one year ago. When he’s doubling headcount over a sustained period (i.e., every 9 months or every 18 months), then growth becomes exponential. At this pace, the role of the engineering leader is to ensure that all teams work efficiently. 

The three teams that you managed last year may become nine teams today. With more people and more teams, leaders must ensure that the right processes are in place and that teams are not stepping on each other’s toes.

Related: Read a post by Shane about how he led a 100-person engineering team during the early days of the pandemic.

Do leaders look for industry experience when hiring engineers?

When these fintech engineering leaders are hiring engineers, do they look for fintech industry experience?

Jeff says that he’d never constrain himself by considering only candidates with fintech industry experience. Instead, he wants to find the best and most talented people available. Jeff looks for engineers with an owner’s mindset and a desire for impact. He wants candidates who seek to learn what the company does and how it operates. 

In past roles, Jeff’s company provided engineers with classes on how financial systems work. Only after engineers grasped the bigger picture could they bulid the right software products. The idea is to find people who are super-smart and teach them what they need to know.

Kevin agrees with Jeff’s approach on hiring engineers, but adds that for Product roles, Kevin leans more on industry and domain expertise. That experience creates a shorter on-ramp to delivering a product roadmap and product strategy. 

At the same time, too much industry experience can be a bad thing. Kevin says that there’s no added advantage to someone who’s been in the industry for a long time. Instead, it’s someone from outside the industry who’s more inclined to be disruptive and break things and that can lead to better outcomes.

Working with engineering services partners

Kevin, Shane and Jeff are Lohika clients, so they have experience working with engineering services partners.

When selecting a partner, Kevin looks for companies who are like-minded. For example, do they share principles over software development lifecycle and software testing patterns? Kevin evaluates whether the partner has the right skillset. Do the engineers have specific experience on the technologies your company relies on? Next, Kevin looks at the partner’s leadership. Who is the engineering manager and are they equipped to deliver on our needs?

Shane looks for long-term relationships with his engineering services partners. Shane doesn’t hire partners for a gig—instead, he thinks of embarking together on a journey. 25% of Shane’s original Lohika team (from November 2018) is still on the team today. 60% of the team from mid-2019 is also still on the team. 

The Lohika engineers buy into Shane’s vision and the company’s vision. According to Shane, “They are Lohika employees, but in fact they are TradeIX engineers. The value to me is too big to pass up.”

How to organize teams during rapid scaling

Shane sees younger engineering managers make a common mistake. “They go out and hire 27 engineers and not think about the care and feeding of them,” says Shane. In other words, if those 27 engineers start tomorrow, who’s going to manage them? If there’s not a solid layer of engineering management in place, then the new engineers are set up to fail.

Shane uses a multiplier rule, where every “X” new engineers added to the team get an engineering manager. Right now, “X” is four. So every time four engineers are added, Shane brings on an engineering manager. 

Shane notes that Finance likes this structure, because they can plan out headcount costs in a deterministic fashion. To some degree, Shane likes to plan engineering teams like the military scales troops (i.e., with adequate leadership in place as troops grow in size).

Technology trend: AI and machine learning

In response to a question about current and future technology trends, Jeff highlighted artificial intelligence (AI) and machine learning. While the use of machine learning to train models has been ad-hoc for a few years, companies (including AWS) are now creating industrialized methods that come with the promise of continuous improvement. 

Jeff says that when engineering teams figure out how to incorporate AI and machine learning into how we build and ship products, there will be a rapid acceleration in product quality. In fact, Jeff says that there will be new ways in which we build distributed systems architectures and software.