Senior Data Analytics Engineer

About the Position

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.

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).

Requirements

• 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.
• Experience with fintech domain.

Nice to Have

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

About the Company

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.