Engineering Manager - Data

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

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

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.

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 small and the surface is broad. You'll own everything from real-time streaming to warehouse modelling with a handful of engineers, and prioritisation calls will be yours to make and defend.

We keep the management layer thin. You won't have a large support structure above you removing obstacles, and you're expected to stay close enough to the technical work to lead it credibly.

The ML function is early. You'll be shaping it as it grows rather than inheriting something established, and that means ambiguity about scope, tooling, and direction is part of the job.