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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analytics Engineer - **Company:** SeQura - **Location:** Barcelona, Spain (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, BigQuery, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Dataspaces, Data Warehousing, Software Architecture, Raw Data, Software Engineering, SQL Databases, Cloud Platform System, Snowflake, Git, Data Layers, Data Lineage, Amazon Redshift - **Published:** July 2, 2026 - **Apply:** https://es.indeed.com/viewjob?jk=416412aa7f2234cc ## About the Role * 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. ## Description We are looking for a Senior Analytics Engineer to own the analytics enablement layer across seQura's 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 you'll 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 seQura's 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 * Product mindset: we treat datasets as products, with clear ownership, quality standards, and consumers. * Strong focus on data governance, documentation, and discoverability. * Collaboration with business stakeholders to translate operational concepts into trusted data models. * Analytics Engineering sits at the intersection of business knowledge, software engineering, and data architecture. * Continuous improvement through testing, observability, and reusable modeling patterns. What to expect in the next 90 days Month 1: You'll immerse yourself in seQura's data ecosystem, becoming familiar with our dbt project, semantic models, and governance principles. You'll 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: You'll deliver your first production-ready dbt models for your assigned business domain, applying testing, documentation, and governance best practices. You'll begin contributing to the team's standards around data quality, maintainability, and semantic consistency. Month 3: You'll take ownership of a meaningful portion of the Single Source of Truth within your domain, enabling downstream teams to confidently self-serve reliable data. You'll 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Making Data Warehouses fast. 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