Senior Data Platform Engineer

Spain·Barcelonasenior

Data EngineerData

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Quick Summary

Overview

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

Technical Tools

airflowawsdbtgithub-actionsgrafanajenkinskubernetesmetabaseprometheuspythonterraformci-cddata-analysisdistributed-systemsetlfintech

About the Role

~1 min read

Design and maintain a “Golden Path” for data pipelines, enabling teams to deploy new pipelines in minutes instead of weeks

Embed data governance, observability, lineage, and access control directly into platform capabilities

Improve the reliability, performance, and cost efficiency of our data infrastructure

Lead architectural discussions and mentor engineers on distributed systems and platform design

Evangelize the platform internally to drive adoption across teams

To build and evolve a self-service data platform that empowers engineering teams, analysts, and data scientists to build, discover, and scale their own data initiatives with built-in governance, observability, and minimal friction.

The full lifecycle of data across the company: extraction, ingestion, landing, transformation, governance, and observability
The data infrastructure and platform capabilities that enable teams to create and scale their own data products

Core platform components that ensure data quality, compliance, lineage, and discoverability

The evolution of our data platform into a self-service ecosystem where teams can autonomously manage their data lifecycle

The Data Platform team is a key part of the Data organization, which also includes the Head of Data, Data Governance Lead, Data Science & AI, and Data Analytics teams.

The Data Platform team currently consists of a Data Platform Lead, four Data Engineers, and one MLOps Engineer. They collaborate closely with the Infrastructure Platform team and the rest of the Data organization to build and evolve the company's data platform.

Platform mindset: we build capabilities and tooling, not one-off solutions
Engineers treat internal teams as platform customers
Strong collaboration with Infrastructure Platform, Data Science & AI, and Data Analytics teams
Infrastructure-as-Code and automation are core engineering principles
We are transitioning from a reactive ticket-based model to a product-oriented platform team

Our data platform runs on AWS with Kubernetes (EKS) and everything managed via Terraform and Helm. CI/CD is handled with GitHub Actions and Jenkins. We ingest data from multiple sources using Airbyte, store it in S3 and Redshift, transform it with dbt, and orchestrate pipelines with Airflow. For governance and discovery, we use OpenMetadata, while analytics are delivered through Metabase. Observability is implemented with Prometheus, Grafana, Thanos, Elastic, and Tempo, and our infrastructure and automation scripts are mainly written in Python, Terraform, and Helm.