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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer (Python/Scala) - **Company:** Super - **Location:** Barcelona, Spain - **Salary:** €60,000.0 - €90,000.0 - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, A/B Testing, Artificial Intelligence, Airflow, Continuous Integration, Software Debugging, Github, Python (Programming Language), Prometheus, Service Layer, Software Engineering, SQL Databases, TypeScript, Workflow Management Systems, ReactJS, Snowflake, Grafana, Backend, Git, Fastapi, Kubernetes, Production Code, Power Analysis (Cryptography), Api Design, Data Pipelines, Docker - **Published:** September 5, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Strong Python engineering skills, including well-tested production code, API development (FastAPI or similar), and sound software design. + Solid experience with a modern data stack: workflow orchestration (Airflow or similar), a cloud data warehouse (Snowflake preferred), and SQL you can debug and optimise under real data volumes. + Experience running services in production, including CI/CD pipelines, containerisation, Kubernetes or gitops-style deployments, and observability. + A data quality mindset - you notice when two numbers that should match do not, and you dig until you know why. + Clear written communication, as RFCs, technical documentation, and async collaboration are core to how this team works. Nice to have + Familiarity with experimentation concepts: A/B testing, exposure vs. assignment, SRM, power analysis, CUPED, holdouts. + Experience with a data catalogue or metadata platform (DataHub/Acryl or similar). + Comfort making changes across the stack, including a TypeScript/React UI when the project calls for it. + Experience building platforms or tooling for internal engineering customers. ## Description We are on a mission to pioneer the world's next era of play. As we grow across Europe and Latin America, we're building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences. Join us, and help make it the most widely used platform in the world! From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day. You will join the Scaled Experimentation team, who build and run Super Technologies' internal Experimentation Platform: the system every product and engineering team uses to run trustworthy A/B tests, holdout experiments, and feature flags at scale. As a Data Engineer, you will own the data backbone of this platform, with the Metric Store and evaluation data pipelines at its centre, spanning everything from Airflow DAGs and Snowflake models to FastAPI services, CI/CD, and the integrations that keep metric definitions in sync across DataHub, GitHub, and the platform UI. What the role involves + Own and evolve the Metric Store: the metric definition repository, its FastAPI service layer, and the Git-backed, PR-based workflows that let users create and edit metrics through the platform UI. + Build and maintain evaluation and monitoring pipelines in Airflow on Snowflake, covering experiment evaluation, SRM detection, exposure log processing, and alerting. + Deploy services to production end to end, including Docker images, gitops-based Kubernetes deployments, GitHub Actions CI/CD, and monitoring with Prometheus and Grafana. + Investigate data quality and trust issues, reconciling exposure counts across tables and dashboards, debugging SRM signals, and validating bucketing and hashing behaviour. + Collaborate with backend engineers, data scientists, and the product manager, contributing RFCs and design docs and reviewing experiment setups for internal teams. + Improve developer experience for platform users through documentation, data exports, and tooling, including MCP and AI-assisted interfaces to experiment data. ## Related Videos - [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) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)