Tech Lead DataOps

Talan is an international advisory group on innovation and transformation through technology, with 5000 employees, and a turnover of 600M€.

We offer our customers a continuum of services to support you at each key stage of your organization's transformation, with 4 main activities:

• CONSULTING in management and innovation : supporting business, managerial, cultural, and technological transformations.
• DATA & TECHNOLOGY to implement major transformation projects.
• CLOUD & APPLICATION SERVICES to build or integrate software solutions.
• SERVICE CENTERS of EXCELLENCE to support the latter through technology, innovation, agility, sustainability of skills and cost optimization.

Talan accelerates it's clients' transformation through innovation and technology. By understanding their challenges, with our support, innovation, technology and data, we enable them to be more efficient and resilient.

We believe that only a human oriented-practice of technology will make the new digital age an era of progress for all. Together let's commit!

We are looking for aTech 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 arounddbt, 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 designApache 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.

Requirements

• Methodology: Strong knowledge and hands-on experience withData Vault 2.0 methodology applied to enterprise data platforms.

• Core Tech Stack: Advanced proficiency inPython (focused on data platforms/automation) andSQL query optimization.

• Data Ecosystem & Orchestration: Solid hands-on experience withdbt andApache Airflow (including DAG design and workflow optimization).

• CI/CD & Cloud Infrastructure: Good knowledge of DevOps practices usingGit/GitLab, Jenkins, Docker, Kubernetes, andSnowflake (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 likeAutomateDV andDBSchema.

• Practical exposure to modern observability and data platform reliability practices.

• Experience or strong interest in integratingAI/LLM tooling (such as GitHub Copilot) into DataOps development and deployment workflows.