About This Role:

We're looking for a Senior, dbt-specialised Analytics Engineer to build and maintain the business-critical data models behind the client's credit, payments, and fraud/AML reporting. Our client is a fast-growing European fintech company in the business spend management space — corporate cards and related financial products — serving SME and mid-sized business customers across the EU and UK, in a regulated environment with GDPR compliance obligations. Engineering is organized into cross-functional squads, with platform/enabling teams providing shared tooling horizontally. The client already runs an AI-native engineering practice: Claude Code and GitHub Copilot are used daily, supported by a growing library of shared, versioned Skills embedded in key repositories, enabling an end-to-end flow from ticket to implementation, testing and PR in several codebases. AI tool/connector rollout follows an approved-list and pilot process. The client is AWS-first overall; for data platform and analytics workloads it also runs GCP, with BigQuery as the primary data warehouse. Day-to-day coordination is Slack-first, with Linear for ticket tracking, GitHub for code/PR review, and Notion as the knowledge base.

Key Responsibilities

Build and maintain business-critical dbt data models.
Model data powering credit, payments, and fraud/AML reporting (working from aggregated/modeled data, not raw access to real customer/transaction data — see note above).
Apply strong SQL and data-modelling skills.
Multiply the output and quality of the squad you join, and share patterns/practice beyond your immediate team so adoption compounds across the organisation.
Embed directly into a client squad as a hands-on Individual Contributor (not a coaching/managerial role).

Must-Have Skills:

Experience with dbt.
Strong SQL and data-modelling skills.
Experience building and maintaining business-critical data models for credit, payments, and fraud/AML reporting.
BigQuery — the client's stated primary data warehouse for data platform and analytics workloads.

Nice-to-Have Skills:

Git — standard, expected tooling, but not explicitly named in the RFP for this role.

We offer*:

Flexible working format - remote, office-based or flexible
A competitive salary and good compensation package
Personalized career growth
Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
Active tech communities with regular knowledge sharing
Education reimbursement
Memorable anniversary presents
Corporate events and team buildings
Other location-specific benefits
not applicable for freelancers

Senior Data Analytics Engineer

Job Description