> Markdown version of [/jobs/ext/1360395-tech-lead-dataops](https://www.wearedevelopers.com/jobs/ext/1360395-tech-lead-dataops). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Tech Lead DataOps - **Company:** Talan SAS - **Location:** Málaga, Spain (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Cloud Computing, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Dataspaces, Data Vault Modeling, Data Warehousing, DevOps, Python (Programming Language), Scrum Methodology, DataOps, SQL Databases, Systems Integration, YAML, Enterprise Data Management, GitHub Copilot, Delivery Pipeline, Large Language Models, Snowflake, HybridCloud, Gitlab, Git, Kubernetes, Deployment Automation, Data Management, Docker, Jenkins - **Published:** July 20, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Methodology: Strong knowledge and hands-on experience with Data Vault 2.0 methodology applied to enterprise data platforms. + Core Tech Stack: Advanced proficiency in Python (focused on data platforms/automation) and SQL query optimization. + Data Ecosystem & Orchestration: Solid hands-on experience with dbt and Apache Airflow (including DAG design and workflow optimization). + CI/CD & Cloud Infrastructure: Good knowledge of DevOps practices using Git/GitLab, Jenkins, Docker, Kubernetes, and Snowflake (or equivalent MPP platforms). + Leadership & Soft Skills: Strong transversal technical leadership, an autonomous/proactive mindset, and excellent communication skills to collaborate with multicultural, distributed teams. + Languages: Professional proficiency in both English and French is required. Nice to have skills + Experience with specific data modeling and automation tools like AutomateDV and DBSchema. + Practical exposure to modern observability and data platform reliability practices. + Experience or strong interest in integrating AI/LLM tooling (such as GitHub Copilot) into DataOps development and deployment workflows. ## Description We are looking for a Tech Lead DataOps to join a large-scale, international program focused on building and scaling the Group's Enterprise Cloud Data Warehouse. The platform centralizes global data across major business domains (Finance, Customers, Operations, etc.) using modern cloud data architectures and Agile/Scrum methodologies. In this role, you will lead the transversal platform and reliability efforts, driving industrialization, automation, CI/CD pipelines, and overall platform stability in a fully remote, multicultural environment. Main Responsibilities Technical Leadership & DataOps Governance + Define and evolve technical standards, ensuring the reliability, scalability, and performance of data workflows. + Lead industrialization and automation initiatives across development and deployment processes, supporting multiple squads on DataOps best practices. + Contribute to technical roadmap definitions, architecture decisions, and continuous ecosystem improvements. Automation & Platform Engineering + Design, maintain, and evolve internal development and deployment tooling around dbt, Airflow, and Snowflake. + Develop and optimize internal CLI tools for automated dbt model generation, YAML testing, DAG creation, and deployment automation. + Contribute to the integration of AI/LLM capabilities into development and DataOps workflows to reduce manual operations. CI/CD & Deployment Engineering + Design, implement, and maintain secure and automated multi-environment Data CI/CD pipelines. + Ensure deployment quality during release cycles, collaborating with project squads and supporting release governance. Orchestration, Reliability & Operations + Supervise, optimize, and design Apache Airflow orchestration workflows and execution DAGs. + Implement monitoring, alerting, and observability capabilities to maximize platform stability and operational efficiency. + Contribute to incident resolution and root cause analysis. ## Related Videos - [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) - [CI/CD with Github Actions](https://www.wearedevelopers.com/videos/856-ci-cd-with-github-actions) - [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) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)