Senior Analytics Engineer

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

We are looking for a Senior Analytics Engineer to own the analytics enablement layer across seQuras core business domains building the trusted well-governed data foundation that business product and data science teams rely on to make decisions and ship models.

This role sits at the intersection of data modeling business domain understanding governance and semantic precision. You will design and deliver production-grade dbt models shape the semantic layer to be AI-ready and set the quality bar for the analytics enablement team. You will work closely with business circle leads Data Scientists across Risk Finance Payments and Collections and the Platform and Data Engineering team.

What challenges youll be solving

• Championing a single source of truth for core business metrics unifying definitions socializing and governing the canonical KPIs used across seQura so that whether a metric appears in a dashboard a DS model or an executive report it means the same thing.

• Building domain data products that serve both operational reporting and ML feature needs ensuring downstream teams can self-serve reliably.

• Designing building and shipping production-grade dbt models across multiple business domains Payments Debt Collection Risk and Finance writing clean well-tested and well-documented SQL.

• Owning the governance layer: data contracts column-level lineage tests and semantic definitions not just shipping models but making them trustworthy.

• Structuring the semantic layer to be machine-readable and AI-ready supporting both self-serve BI and AI-powered use cases.

• Setting and upholding standards for testing documentation and data quality across the analytics engineering team.

• Collaborating closely with Data Scientists circle leads and the Platform team to understand domain logic deeply and translate it into precise unambiguous data models.

• Making cross-domain architectural decisions with a systems mindset thinking beyond individual models to how the full data layer fits together.

About the Data team

Team mission

To turn seQuras raw data into trusted well-governed data products that power decision-making across the company.

The team builds the foundations that enable Product Finance Risk Payments Data Science and AI teams to work with reliable consistent and discoverable data ensuring everyone speaks the same language when making decisions.

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

The Analytics Engineering team consists of four Analytics Engineers working alongside a Data Governance Lead and reporting to the Head of Data.

The team partners closely with business stakeholders across Risk Finance Payments and Collections while collaborating with Data Platform Data Engineering and Data Science teams to ensure data products are reliable scalable and easy to consume.

How we work

1. Product mindset: we treat datasets as products with clear ownership quality standards and consumers.

2. Strong focus on data governance documentation and discoverability.

3. Collaboration with business stakeholders to translate operational concepts into trusted data models.

4. Analytics Engineering sits at the intersection of business knowledge software engineering and data architecture.

5. Continuous improvement through testing observability and reusable modeling patterns.

What to expect in the next 90 days

Month 1:

Youll immerse yourself in seQuras data ecosystem becoming familiar with our dbt project semantic models and governance principles. Youll work closely with business stakeholders and fellow Analytics Engineers to understand your domain existing data models and opportunities to improve consistency and data quality.

Month 2:

Youll deliver your first production-ready dbt models for your assigned business domain applying testing documentation and governance best practices. Youll begin contributing to the teams standards around data quality maintainability and semantic consistency.

Month 3:

Youll take ownership of a meaningful portion of the Single Source of Truth within your domain enabling downstream teams to confidently self-serve reliable data. Youll also contribute to cross-domain initiatives helping define shared semantic models governance standards and reusable data patterns across the organization.

Tech stack & environment

Our modern data platform is built on AWS with dbt at the heart of our transformation layer and Redshift as our analytical data warehouse.

Analytics Engineers work extensively with SQL and dbt to build trusted reusable data models while leveraging Git-based development workflows automated testing documentation and data quality monitoring to ensure reliable data products.

The team collaborates closely with Data Platform and Data Engineering building on top of cloud-native infrastructure and contributing to a governed data ecosystem designed to support analytics business intelligence and AI use cases across seQura.

What youll need

• 5 years of experience as an Analytics Engineer with a strong track record of building and delivering production-grade data models (mandatory).

• Genuine curiosity about the business domains you model you want to understand what decisions are made using the data not just how the data looks

• Strong hands-on dbt experience: modeling testing data contracts documentation not just running dbt but understanding how to design a reliable model layer (mandatory).

• Expert SQL on a modern cloud data warehouse (Redshift BigQuery Snowflake or similar).

• Systems thinking: ability to make cross-domain architectural decisions not just implement individual models. You see how the pieces fit together.

• Semantic precision: you write metric definitions and data documentation that are unambiguous to both humans and AI agents.

• Experience with Python for data tasks is a plus.

• Familiarity with data governance and lineage tooling (OpenMetadata or similar) is a plus.

• Fintech or financial services domain knowledge is a plus.

• English proficiency required. Spanish is a plus.

What we offer

We have a strong and sustainable foundation where we provide a secure and reliable workplace. You have the freedom and trust to make the best contribution possible.

One of our most valued strengths by our employees is our fellowship and supportive culture which fosters a sense of belonging by working closely with our values. With us you will have challenging projects to work on and push your skills and knowledge.

In addition we are very proud of the unique office we have which offers a comfortable and inspiring environment to work in with everything you need.

• 23 vacation days 2 days of free disposal per year.

• Flexible compensation plan for transportation restaurants and kindergarten with Cobee.

• Health insurance discounts with Sanitas and DKV.

• Flexible working hours.

• A personal budget for professional development.

• Office workshops and meet-ups to encourage community participation and career growth.

• Hybrid or remote work (up to 2 hours difference from GMT2).

Moreover we have a Wellness Program that embraces a holistic approach by covering 6 areas (occupational physical financial emotional social and environmental consciousness). Each area will include a variety of activities and youll be able to choose from 34 different activities that best meet your needs to configure a plan that best works for you.

Which are the next steps

• Interview with People Team

• Hiring Manager interview

• Technical take-home assessment (async)

• Live Peer review of the assessment

• Meet the team

We kindly ask that you submit your CV in English as it is the official language of our community.

We promote equal opportunity to all regardless of age color gender identity medical condition physical or mental disability race religion sexual orientation or any other characteristic. We have an inclusive environment and respect is above all.

Do you want to be part of the change Join us!