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# Senior Data Analyst (with AI experience)

- **Company:** N-iX
- **Location:** La Unión, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, BigQuery, Data Infrastructure, Data Warehousing, Github, Issue Tracking Systems, Standard Sql, Transaction Data, GitHub Copilot, Git
- **Published:** August 5, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=896db8a5279e9a17

## About the Role

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.

## Description

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., 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).


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