Senior Platform Engineer (Data team)

About seQura

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

We make a difference in sales performance by tailoring our solutions to different verticals (Retail, Education, Optics, 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 in the scaleup phase. Present in southern Europe and Latin America, we are growing above 50% CAGR and with more than 100 Million in Annual Recurring Revenue. Over 5000 businesses, more than 2 million shoppers, and 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 4.5/5, and a Glassdoor rating of 4.3/5.

About the role 🤓

We're looking for a Senior Platform Engineer to join our Platform Engineering team and help us scale the infrastructure, tooling, and platform capabilities that power software development across seQura.

Your mission will be to help evolve our Data Platform , enabling engineering teams to ingest, transform and operate data pipelines efficiently through self-service capabilities, automation, and platform abstractions.

You'll design the guardrails, golden paths, and platform services that improve developer experience, increase reliability, and reduce operational complexity across the organization.

You'll work in a cloud-native environment on AWS, where Infrastructure as Code, observability, automation, and platform thinking are fundamental engineering principles.

What challenges you'll be solving 🚀

• Design and evolve the infrastructure under the Data Platform, enabling data engineering teams to provision infrastructure and operate services autonomously.
• Identify friction points for data scientists, data engineers and software engineers and build platform capabilities that improve developer experience and productivity.
• Design and maintain Golden Paths for pipelines creation, deployment, observability, and operational excellence.
• Build self-service workflows that reduce operational overhead and accelerate delivery across teams.
• Embed security, reliability, observability, compliance, and access control directly into platform capabilities.
• Improve the scalability, performance, resilience, and cost efficiency of our data infrastructure.
• Lead architectural discussions and mentor engineers on cloud-native architectures, distributed systems, and platform engineering practices.
• Evangelize platform adoption internally and drive platform-as-a-product thinking across engineering teams.

About the Data team 🧩

Team mission

To Provide a reliable self-service ecosystem where data is treated as a high-quality product, empowering every team to build, discover, and scale their own data initiatives with built-in compliance and zero friction.

What we own

• AWS cloud infrastructure under the data platform (Redshift, S3, Postgres, Sagemaker…)
• Kubernetes applications like Airbyte, Airflow, Metabase or OpenMetadata
• Infrastructure as Code foundations and reusable infrastructure modules.
• CI/CD tooling and deployment automation.
• Observability, monitoring, logging, and alerting definitions.

Team Structure

The Data Platform team works closely with product and data engineering teams across the company, providing shared infrastructure, deployment tooling, observability, and platform services.

How we work

• Platform mindset: we build reusable capabilities, not one-off solutions.
• Data Analytics, Data Scientists and other tech teams are our platform internal customers
• Strong focus on user experience and self-service.
• Infrastructure-as-Code and automation are core engineering principles.
• Product-oriented platform ownership with measurable impact on data quality.
• Continuous improvement through feedback loops and platform adoption metrics.

What to expect in the next 90 days 🏁

Month 1

You'll immerse yourself in our data platform ecosystem, infrastructure, and engineering workflows. You'll meet data analytics and Data Science teams to understand their challenges and identify friction points in their day-to-day.

You'll contribute early improvements to our Infrastructure-as-Code, ingestion or orchestration stack, CI/CD pipelines, or observability tooling, with the goal of delivering a tangible data quality improvement.

Month 2

You'll start contributing to the long-term data platform roadmap. You'll help define and implement standardized ingestion patterns, reusable abstraction modules, and self-service workflows that reduce cognitive load for data engineers, data scientists and data analytics teams.

Your goal will be to simplify and accelerate how teams build and operate data platform.

Month 3

You'll lead a strategic initiative from design to deployment, such as automating components upgrade, stablishing a data contract solution or migrating to CDC ingestion.

During seQura Week (when the whole company gathers at our Barcelona HQ), you'll present your work to the engineering organization, demonstrating how data platform improvements enable teams to use data in a more reliable, accurate and efficient way.

Tech stack & environment 🛠️

Our infrastructure runs on AWS with Kubernetes (EKS) and everything managed via Terraform and Helm. CI/CD is handled through GitHub Actions and Jenkins. Our observability stack includes Prometheus, Grafana, Thanos, Elastic, and Tempo. Infrastructure automation and platform tooling are primarily built using Python, Terraform, Helm, and Kubernetes-native technologies.

Our data platform is composed by Sagemaker for MLOps, Redshift as datawarehouse, Airflow for orchestration and Airbyte for ingestion. Apart from that Openmetadata and Metabase help users consume the data we ingest and process.

Apart from that we use some agents running on AWS with Bedrock to simplify day to day processes.