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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Fever's Blog | Tech.eu - **Location:** Madrid, Spain - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Big Data, Data Infrastructure, Extract Transform Load (ETL), Dataspaces, Data Warehousing, Digital Assets, Python (Programming Language), PostgreSQL, Machine Learning, Pair Programming, Standard Sql, Software Construction, Data Logging, Snowflake, Data Strategy, Pandas, Amazon Relational Database Service, Pyspark, Data Management, Machine Learning Operations, Data Pipelines - **Published:** July 17, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role You are smart, get stuff done, have great energy, and thrive in a fast paced environment You are an expert working with large volumes of data from multiple sources, both internal and external (e.g. APIs) You are comfortable with software engineering best practices and you are proud to write robust, readable and maintainable code. You are an expert in Python3 and its data ecosystem (e.g. Pandas, Pyspark) Strong knowledge of SQL (PostgreSQL, Snowflake, …) Experience with AWS (S3, EC2, RDS, …) You are proficient in business English It would be a plus if you... You have worked with Airflow or similar tools. You have worked in a multidisciplinary team working with roles such as data analysts, data scientists, marketing or product managers. You are familiar with tools and processes for reproducible, production-ready ML applications (e.g. data logging, ML workflows, model serving). You have worked with the API of the most popular marketing tools (Facebook, Google, Instagram, etc.) You have built data-powered tools either in a professional setting or as passion projects. ## Description You'll be part of the Data organization, building and operating the core technologies that enable data scientists, analysts and the different business units to leverage rich data in efficient and innovative ways to generate impact and connect people to the most relevant real-world experiences. You'll own critical data pipelines for our Data Warehouse and the resulting data products that are used daily across the company to inform all sorts of decisions and models. You'll ideate and implement tools and processes that increase our ability to exploit our diverse sources of data to solve business problems, understand behaviors, … You'll work with the latest Big Data and Machine Learning technologies to enable new use cases and continuous innovation. On your first month in Fever: You will be fully integrated into the team. During this month you will have already participated in onboarding, pair programming, one to one, Scrum sessions, and you will have met the different departments at Fever You will get familiar with Fever's tech stack and frameworks used to develop our data strategy You will attend some of the Fever Original's experiences like Candlelight After 3 months in Fever: You'll be able to come up with solutions to new difficult problems and you'll be generating impact and creating new business opportunities. You'll have responsibilities and ownership over parts of our Data Warehouse or other critical tools. You will participate in some of the hackdays or hackathons we organise with other teams, and you will mostly know everybody from the data and engineering communities. On your 6th month in Fever: You'll contribute to the overall health of our data ecosystem, improving performance, scalability, robustness, … You'll be able to identify gaps in our platforms and processes and be a champion for continuous improvement You'll be mentoring other new joiners to the team You will participate in some of the team buildings we organise for your team or the whole engineering team, Build trusted data assets that power Fever's decision making Have a business-oriented mindset and create data automatization to create huge business opportunities Design, build and support modern and scalable data infrastructure, e.g.: Write robust, maintainable code to orchestrate our ETL workflows. Build data quality monitoring processes Build data tools to make our analysts and scientists more efficient Collaborate with other engineers, and stakeholders to understand what data is required and how best to make it available in our Data Platform. 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