Software Engineer - Data (All Genders)

PREMATCH Sports GmbH · Kölnsenior

En corto

▸Construyes y mantienes pipelines de datos para escalar el negocio de Teamwear.
▸Trabajas con fuentes reales y desordenadas (web, imágenes) para generar datos confiables.
▸Eres el puente técnico entre ingeniería y equipos de negocio, con libertad para definir prioridades.

You speak English fluently

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¿Qué piden?

✓6-8 años de experiencia en ingeniería de software y datos
✓Experiencia con pipelines automatizados, scraping y procesamiento de imágenes
✓Dominio de bases de datos y modelado de datos
✓Capacidad para trabajar de forma independiente sin supervisión constante
✓Enfoque práctico: sabes cuándo 'bien y rápido' es suficiente
✓Capacidad para comunicar incertidumbres y detalles de calidad en los datos

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonPostgreSQLRabbitMQGrafanaMetabasePulumiKubernetesAWSLLM APIsWeb scraping

¿A quién escribirle en PREMATCH Sports GmbH?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Hey! Imagine amateur football finally had the platform it deserves. Every weekend, millions of people write their own personal football stories – on pitches, in clubs and on the sidelines. We believe this passion deserves a digital home too. That's exactly what we're building every single day. tl;dr We’re looking for someone to help make our Teamwear business scalable beyond its current scope. Tech Is this a match? The most important basics – so we both know if it could work. 100% remote: This role is generally available 100% remote within the EU or UK. If you'd still love to work from one of our offices in Cologne, Berlin or London - you're more than welcome. Experience: You bring 6-8 years of experience in software engineering and data engineering. Languages: You speak English fluently - German is a big plus. Startup spirit: You'd rather shape things than just tick boxes - even when that means figuring stuff out without a finished roadmap. What you'll do - your playing field This is how you move our mission forward every day. Data Pipelines: You build and maintain data pipelines for automated data collection, structured so that new sources and markets can be added quickly. Algorithms: You develop algorithms to extract relevant information from websites and images. Data Quality: You ensure data quality through validation, confidence levels, monitoring, and clean handovers internally and to partners. Data Infrastructure: You manage and further develop the data infrastructure of the Teamwear area. Technical Point of Contact: You act as the second technical point of contact for the Teamwear area. In addition to your core responsibilities, you will also: Internal Tools: You develop internal tools that enable the team to collect and correct data themselves. Analytics & Reports: You create analytics and reports for internal stakeholders and external partners. What you bring to the team We're not looking for a perfect CV. But you should recognise yourself in these points. Broad Technical Skillset: You move between data pipelines, backend and some frontend, and are not afraid of AI or image analysis. Data Automation: You have experience with automated data pipelines, web scraping and image processing. Databases & Data Modelling: You have expertise in databases and data modelling. Independent Working: You work independently, even when there is no second engineer in the room and the context comes from business teams. Fast Learner: You quickly get familiar with unfamiliar domains and ask the right questions along the way. 80/20 Mindset: You know when fast and good enough is the right approach and when the data needs to be 100% accurate because a decision is based on it. Data Quality: You care about details when it comes to data quality and make uncertainties explicit. Data-Driven Thinking: You don't stop at analysing the data. You identify what is in the data, what it means and what we should look at next - even if nobody asked. Engineering Practice: You write clean, maintainable code and follow good engineering practices such as testing and code reviews. Tools: Python, PostgreSQL, RabbitMQ, Grafana, Metabase, Pulumi, Kubernetes, AWS, LLM APIs Good to know We want you to know what this role really looks like from day one. If you are looking for clearly defined tickets, a large engineering team and a finished tech stack, this role is probably not for you. You will work largely as a team of two within Engineering. Most of your day-to-day work will be with business people, not engineers. You have two connections: technically to Engineering and on the content side to the Teamwear team. This gives you a lot of freedom and means you need to set your own priorities. A large part of the role is data collection and data quality, rather than greenfield ML. The exciting part is turning messy, real-world sources into reliable data.

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