Senior Backend Go Developer

Responsibilities
You will become a member of a global Tech team located in Oslo, Helsingborg and Lisbon, building a multilanguage, highly scalable, cloud native, multi market broker and membership platform.
Owns significant backend features end-to-end on the One Platform core of the Money & Volume business - Go services on AWS. Works in an AI-First engineering culture where Claude Code and AI-assisted development are daily workflow, not optional extras. Bridges product thinking, strong  architecture instincts, and practical delivery: shipping multiple features a week while maintaining the quality standard  that financial data demands. Contributes to architectural direction, shares knowledge with peers (we learn together rather than in a hierarchy) and collaborates across product, front-end, data and our bank partners' integration teams.

How we build: One Platform is an event-driven, cloud-native platform — Go services on AWS Lambda with EventBridge and SQS, relational data on RDS, infrastructure as code, trunk-based development and a green pipeline as table stakes. It carries ~1.5M applications a year through bank integrations in four markets, and it is being connected to the group's Central Customer Platform (CCP), which provides shared customer identity and single sign-on across the Axo brands. AI-assisted development is the default daily workflow, and much of what we ship goes out flag-gated so we can measure it - and may be deleted once the data speaks. Experiments exist to move commercial outcomes (submissions, approvals, disbursements, gross profit — not engagement for its own sake)

We are not looking for someone who has memorized this exact setup — it is bespoke. We want someone with the underlying instincts (clear domain boundaries, idempotent event handling, data-model discipline, knowing when to experiment vs. when to build to last) who is energized rather than unsettled by an opinionated architecture. 

Your responsibilities will grow to match your abilities and experience.
Own complete backend feature development from spec through deployment — Go services on AWS
  • Lead architectural discussions and document the decisions that matter — in the lightest form that does the job  • Build and review AI agent workflows using Claude Code, or equivalent tools — delegate implementation, review all output  • Design and operate scalable, event-driven services — APIs, queues and jobs (Go on Lambda, EventBridge, SQS)  • Integrate with partner banks, credit data providers, the Central Customer Platform (CCP) and internal platform services  • Instrument what you ship and read the results — event tracking, experiment readouts, and the judgment to drop what did not move the number  • Enforce GDPR and EU AI Act compliance requirements in every feature — data privacy is architecture, not a checkbox
  • Share knowledge with peers — reviews, pairing, showing how you solved something; set the bar for code quality and AI-assisted development practices
 Qualifications
Must have:
AI Tooling & Development Workflow  • Strong motivation to make AI-assisted development their primary workflow - agentic coding (Claude Code, Cursor, or equivalent) is our daily way of working, and the candidate must be eager to learn and master it  • Structured, critical approach to AI-assisted engineering: analyses the problem, proactively gathers the information a plan needs, plans the solution and picks the right tool for the job — then reviews all AI generated output critically rather than accepting it  • Judgment about method: knows when a change needs a written plan and when it is too small or too unshaped for one — matches the process to the problem, not the other way round
Backend — Go
  • 5+ years backend experience; 2+ years of production Go strongly preferred - strong backend engineers from an adjacent typed/compiled language (e.g. Rust, Java, C#, Kotlin) willing to move to Go will be  considered  • Strong command of Go concurrency — goroutines, channels, context — and idiomatic Go  • Solid API design (REST and/or gRPC) with clear domain boundaries; domain modelling and resource-oriented API design  • Writes clean, structured code using best practices (SOLID, clear domain boundaries, DTOs)  • Deep relational-database experience: schema design, performance, query optimization  • Experience designing scalable, event-driven systems (queues, jobs, APIs, integrations) — including idempotency and failure handling  • Knowledge of testing (Go test, table-driven) and static analysis tooling  • Comfortable with debugging, performance tuning, and zero-downtime deployments
Cloud-native — AWS
  • Cloud-native on AWS — Lambda, EventBridge, SQS, RDS (serverless, event-driven patterns)  • Comfortable operating services in production — monitoring, alerting, incident response  • Infrastructure-as-code and CI/CD for backend services  • Containerisation (Docker) and service-to-service integration  • Treats a green pipeline and trunk-based development as table stakes — small, frequently-merged changes, not long-lived branches
Comfort with ambiguity — two gears
  • Thrives without a fully-specified ticket: takes a loosely-defined problem and fills in the blanks with sensible product and technical calls — then validates, rather than stalling for perfect requirements  • Outcome-driven: cares whether shipped work moved the number, not just that it shipped — comfortable having work evaluated on measured outcomes  • Experiment gear: ships fast, deliberately disposable slices behind feature flags for A/B tests, optimizing for learning velocity — comfortable with “good enough to measure”, and with throwing it away if the data says so  • Foundation gear: builds durable, well-tested, reusable services once something has earned its place  • Treats “which gear is this?” as a conscious decision, not a personality default — the senior skill is telling a throwaway experiment apart from a load-bearing foundation
Nice to have:
  • Existing daily use of agentic coding tools - able to describe and demonstrate a personal AI development workflow and tooling setup (e.g. CLAUDE.md, project rules, context engineering) - is a strong plus, not a prerequisite  • Proficiency in working with LLM skills and MCP servers — building or extending them shows the ability to integrate AI into normal workflows in a structured way, and an attuned sense of where AI is strong and where it is not  • Experience in regulated / fintech environments (GDPR, handling financial/personal data); OAuth2/OIDC and strong-authentication flows  • Familiarity with the surrounding stack for integration touchpoints - TypeScript/React on the front-end, PHP/Laravel in legacy services  • Observability tooling (metrics, tracing, structured logging — we run Sentry and CloudWatch); cost- awareness in serverless design  • Feature-flag / experimentation platforms (e.g. Statsig) and behavioural analytics - reading an experiment result, not just shipping the flag  • Multi-brand / multi-market product experience; third-party integration work with partner APIs (banks, credit bureaus, CRM)  • Comfortable instrumenting what they ship - event tracking for measurement, and reading experiment results to know whether it worked
Our stack — context, not a bar to clear. You'll ramp into this in your first weeks — prior familiarity is a plus, not a requirement: Go on AWS Lambda, EventBridge and SQS; RDS; infrastructure as code; GitLab CI; Sentry and CloudWatch for observability; Statsig for experimentation.
Education
BSc/MSc in computer science, engineering or related discipline or extensive relevant work experience
Travelling
Could happen but not often
Remarks
Soft Skills
  • Systems thinker: sees how individual components affect the whole product and business  • High ownership: takes features from first line to production — does not throw over the wall  • Constructive scepticism: questions assumptions, including AI-generated output  • Intellectually honest: flags blockers early, estimates with ranges, communicates uncertainty  • Self-directed: drives their own work in a low bureaucracy environment; does not wait to be told what to do  • Committed to continuous learning: the AI tooling landscape changes weekly; adaptability is required  • Balances technical excellence with delivery velocity - knows when good enough ships faster and when quality debt is dangerous in a financial product