• Lead the Design and Implementation: Architect and maintain high-performance, secure, and scalable data pipelines to support AI-driven workflows. • Drive Integration: Collaborate with cross-functional teams to autonomously manage the ingestion, transformation, and integration of large datasets from external partners. • Enhance Data Quality and Compliance: Build systems to ensure data quality, integrity, and regulatory compliance (e.g., ISO 13485, GDPR). • Build and Automate: Develop reusable code and frameworks to streamline data processes and ensure operational excellence. • Monitor and Troubleshoot: monitor data pipelines, resolve issues autonomously, and implement safeguards to minimize disruptions. • Innovate: Identify and implement best practices and emerging technologies to enhance data infrastructure and team efficiency. • Collaborate Globally: Serve as a technical expert, working closely with Product, Engineering, and Data Science teams to ensure alignment on project goals and deliverables.

• Build and operate data pipelines • Design and build pipelines that support data movement across systems - ingestion, transformation, compliance, and cross-domain data flows • Own pipeline operations end to end: monitoring, incident resolution, and data quality across both new and inherited workloads • Make sound pipeline design decisions independently while working within the architecture the team has established • Assess and improve existing data systems • Develop working knowledge of how existing infrastructure fits together by mapping data flows, dependencies and performance characteristics • Improve documentation, observability, data quality, and operational standards across the systems you work in • Identify technical debt and reliability risks and bring recommendations grounded in meaningful impact • Build and maintain data infrastructure • Provision and manage the infrastructure your data workloads require, using established IaC practices and team standards • Contribute to shared infrastructure as the platform evolves, building on new foundations as they become available • Maintain and improve the reliability, performance, and cost-efficiency of the infrastructure you own • Contribute across the data platform • Build enough working knowledge of the team's full system footprint to step in when priorities shift or teammates are unavailable • Contribute to platform work as needed — you won't own the new build, but you should be ready to support it when the team needs flexibility

Role Description In this role, you will help shape the data foundations behind AI-driven products and workflows. As a Senior Data Engineer | Data Ops, you will play a key part in connecting complex data sources, strengthening the reliability of critical systems, and partnering closely with Product, Engineering, and Data Science to support high-impact work across the business. - Lead the Design and Implementation: Architect and maintain high-performance, secure, and scalable data pipelines to support AI-driven workflows. - Drive Integration: Collaborate with cross-functional teams to autonomously manage the ingestion, transformation, and integration of large datasets from external partners. - Enhance Data Quality and Compliance: Build systems to ensure data quality, integrity, and regulatory compliance (e.g., ISO 13485, GDPR). - Build and Automate: Develop reusable code and frameworks to streamline data processes and ensure operational excellence. - Monitor and Troubleshoot: Monitor data pipelines, resolve issues autonomously, and implement safeguards to minimize disruptions. - Innovate: Identify and implement best practices and emerging technologies to enhance data infrastructure and team efficiency. - Collaborate Globally: Serve as a technical expert, working closely with Product, Engineering, and Data Science teams to ensure alignment on project goals and deliverables. Qualifications - 3+ years of experience in DataOps, data engineering, or a similar role, preferably in a regulated industry. - Proficiency with SQL and NoSQL databases and experience designing complex data pipelines. - Expertise in cloud platforms (AWS, GCP, or Azure) and tools like Terraform, Kubernetes, and Docker. - Proven ability to work autonomously, drive projects independently, and deliver measurable results. - Strong communication and leadership skills to mentor team members and coordinate with diverse stakeholders. - Fluency in English and Spanish. Requirements - Familiarity with monitoring tools and data visualization platforms (e.g., Grafana, Power BI). - Exposure to machine learning workflows or AI model integration. Benefits - 🚀 Be part of one of the most disruptive startups in HealthTech and AI and make a difference. - 🏆 Personal and professional growth as part of a fast-growing, international team. - 🎧 No corners cut in having the best tech equipment to do your job. - 🙋 Unlimited remote working environment, where you can choose to work from home and attend our Madrid office whenever you want to. - 📚 Learning and development opportunities and training budget. - 💸 Flexible Remuneration. - 👩🏻‍⚕️ Health Insurance covered by IDOVEN. - 🏋 Access to Wellhub (Gympass), empowering your physical and mental wellness! - 🖥️ Remote Work Allowance for your home office setup! - 🌴 23 holidays, your birthday off, and December 24th and 31st half days to celebrate! - 😎 Regular team events and Thursday happy hours. - And much more! Company Description At Idoven, we believe in hiring top talented people like you - ambitious, forward thinkers who want to make a difference in the world and have an impact. - Diversity & Inclusion: At Idoven, we believe diverse teams build better products. - We are an equal opportunity employer and welcome applicants from all backgrounds, regardless of race, gender identity, age, religion, sexual orientation, or disability.

• Analysierst Kundendaten und entwickelst Machine-Learning-Modelle • Bau und Betrieb von Datenpipelines • Überführung von Modellen in produktionsreife Services • Backend-Entwicklung, wenn erforderlich • Schreiben von wartbarem Code • Übersetzen fachlicher Fragestellungen in konkrete Anwendungsfälle • Aktive Mitarbeit an der Referenzarchitektur