Financial Product Data Analyst

About seQura

seQura provides innovative, flexible and easy-to-use payment technologies that help merchants acquire, convert and retain more customers.

We make a difference in sales performance by tailoring our solutions to different sectors, to address their unique pain points and deliver superior results in Retail, Education, Eyewear, Repairs and Travel.

We also empower smart shopping to consumers who seek more value, convenience, and flexibility in their shopping, with new payment experiences that allow them to save, access interest-free credit, or pay in small, comfortable installments of up to 24 months.

Born in Barcelona, seQura is a privately-owned fintech, currently expanding throughout southern Europe and Latin America, growing above 50% CAGR.

Over 6000 businesses, almost 3 million shoppers, and almost 400 employees continue to rate us as one of the most loved and trusted fintechs out there, with an NPS of 87%, a Trustpilot rating of 4.7/5, and a Glassdoor rating of 4.1/5.

About the role 🤓

Finance at seQura runs on data: recurring calculations, regulatory processes, provisioning, reporting cycles, and profitability tracking. As a Senior Financial Product Data Analyst, you'll own the link between how Finance actually works, the KPIs it tracks, and the decisions those KPIs drive, turning that understanding into reliable, self-service data products.

This isn't a "receive a ticket, write a query" role. You'll work closely with Finance and Finance Tech teams to understand why a number matters before you model it, then build trusted data products that Finance teams can use independently.

Your work will directly improve how Finance operates, helping establish a single source of truth for revenue, enabling self-service reporting, and ensuring the business can make confident decisions based on reliable data.

What challenges you'll be solving 🚀

• Learn seQura's Finance domain deeply enough to understand which KPIs matter, why they matter, and the business decisions they support—from pricing and provisioning to servicer settlement and product profitability.
• Design and build production-grade data products, not just queries: dbt models on Redshift, fully documented and tested to Single Source of Truth standards, together with the Metabase dashboards and self-service datasets that sit on top of them.
• Partner directly with Finance stakeholders to translate complex financial and regulatory processes into robust, reliable, and auditable data models.
• Validate and reconcile critical Finance outputs, including servicer reports, provisioning runs, and amortisation tables, ensuring they are accurate and audit-ready.
• Chase data quality issues back to their source, working closely with Data Engineering to solve problems upstream instead of applying downstream workarounds.
• Help close the existing revenue gap between operational systems and the data warehouse while improving Finance models for defaults and amortisation.
• Present findings, recommendations, and new data products directly to Finance leadership, connecting the data back to the business decisions it's meant to support.
• Contribute to broader Data & AI governance initiatives, including metadata, documentation, and data catalog work wherever it impacts Finance.

• About the Data team 🧩
• Team mission
• To turn seQura's raw data into trusted, well-governed data products that power business decisions, reporting, and AI across all domains.
• What we own

• The dbt transformation layer across business domains.
• The Single Source of Truth (SSOT) for key business metrics.
• Semantic models that enable consistent reporting and analytics.
• Data contracts and governance standards across domains.
• Data lineage, documentation, and discoverability.
• Data quality, testing, and monitoring to ensure trusted data products.

Team Structure

You'll join a team of four Analytics Engineers, working alongside a Data Governance Lead and reporting to the Head of Data.

You'll collaborate closely with FP&A, Accounting, Treasury, Finance Tech, Data Engineering, and the wider Data & AI team to ensure Finance data is accurate, governed, and easy to consume.

How we work

• We treat data as a product, with clear ownership, quality standards, and consumers.
• We build self-service data products rather than one-off reports.
• We understand the business context before building data models—we care as much about why a metric exists as how it's calculated.
• We work closely with domain experts to turn business knowledge into reusable, trusted data assets.
• We continuously improve data quality through governance, documentation, testing, and collaboration.

What to expect in the next 90 days 🏁

Month 1

Learn seQura's core Finance processes and KPIs, including pricing, provisioning, servicer settlement, and product profitability. Meet stakeholders across Finance and Finance Tech, and become familiar with the existing dbt models and Metabase dashboards supporting Finance.

Month 2

Take ownership of your first data quality or reconciliation initiative end-to-end, tracing issues back to their source alongside Data Engineering while contributing to an active dbt model.

Month 3

Deliver your first self-service Finance data product—a documented dbt model together with a Metabase dashboard—that Finance stakeholders can use independently, and present your work and recommendations to Finance leadership.

Tech stack & environment 🛠️

Our Finance Analytics stack is built around SQL, dbt, Redshift, and Metabase, enabling trusted, production-grade data products across Finance.

The team uses dbt for transformation and testing, Redshift as the analytical warehouse, and Metabase to deliver self-service reporting. OpenMetadata supports governance and discoverability, while Python and Airflow are used where automation or orchestration adds value.