Engineering Manager - ML and Data

An Introduction to Primer

Primer is the unified infrastructure for global payments. We give finance and payments teams the visibility and control to reduce complexity, improve performance, and capture more revenue - all from a single platform.

Backed by Sofina, Peak XV Partners, ICONIQ, Tencent, Accel, and Balderton, we're building the payments layer the world's best companies rely on.

Watch our showcase > https://primer.io/the-primer-showcase

Read up on our $100m Series C https://www.primer.io/blog/series-c

Learn more about our culture > https://primer.io/careers

WHICH TEAM WILL YOU BE JOINING?

Every payment that flows through Primer generates data, and the Data team is what turns that torrent into something merchants and the business can actually use. We build and operate the data platform behind Primer's unified payments infrastructure, the event streaming architecture powering real-time payments analytics, the data lakehouse that makes payments data trustworthy and queryable at scale, and the pipelines that feed everything from merchant-facing insights to internal decision-making.

The team is small and senior - a tight group of data engineers, including Staff-level talent, operating with a high degree of autonomy. Alongside it sits Primer's emerging ML function, which will fold into this role's remit as it matures.

You'll lead the Data team as its Engineering Manager owning delivery, growth, and technical direction, reporting into a Senior Engineering Manager and partnering closely with Product and other teams across engineering, finance and revenue operations.

What will you be doing?

• Own delivery end to end across the data platform: streaming, warehousing, modelling, and the pipelines in between, holding the team to a consistent bar on quality and pace without becoming the bottleneck yourself
• Lead, coach, and grow the engineers on your team: career development, feedback, performance, and building an environment where the team does its best work
• Stay technically credible. You'll review designs for real-time streaming systems, evaluate trade-offs in warehouse architecture and data modelling, and tell when a pipeline design won't survive contact with systems at production scale
• Treat data consumers as first-class users. Product teams, finance, and merchants all build on what your team ships, so you'll push for reliable contracts, documented models, and changes that don't silently break downstream
• Take on the ML function as it grows. You'll own its delivery and people leadership, and shape how ML capability develops inside Primer's data organisation.
• Partner with Product and engineering leadership to shape a realistic roadmap, surface risks early, and protect a small team from over-commitment
• Drive operational excellence. Data quality monitoring, incident response, and post-mortems that close out their follow-ups, on systems the whole company depends on
• Own the cost side of the platform. Data infrastructure is one of the fastest-growing line items as Primer scales, so you’ll embed FinOps practices into how the team works - cost visibility, accountability for what you and the product teams run, and drive decisions that weigh spend alongside performance

What we're looking for

• Experience managing a team of Data engineers, with accountability for their delivery, growth, and performance
• Exposure to machine learning. You’ve directly managed or supported ML engineers, understand the lifecycle of models in production, and can credibly lead an ML function as it grows.
• Enough technical depth to be respected by strong engineers. You can interrogate a streaming architecture, spot the wrong trade-off in a data model, and recognise when the information you've been given doesn't add up.
• A background building and operating data platforms at scale - medallion architecture, event streaming, warehousing, transformation frameworks like DBT, ideally where the data directly drives revenue or customer-facing data products
• Experience coaching engineers across a wide seniority range, including retaining and stretching Staff-level talent
• Comfort operating with high autonomy. You resolve ambiguity rather than wait for it to be removed.

Nice to have:

• Experience with payments, fintech, or other finance related domains
• Familiarity with modern cloud data stacks (we work with technologies like Kafka, Snowflake, Firebolt, and DBT), though depth of engineering leadership matters more than any specific tool
• Experience with cloud providers and tooling like AWS, Terraform, Kubernetes
• Familiarity with data mesh concepts. You understand domain-oriented ownership and treat data as a product, driving towards self-serve platforms.

You may not like it here

• Primer is remote-first. There's no office to walk into for context or a read on how things are going, and that applies doubly when you're the one your team looks to for direction.
• The team is …