Job Description
Company Description
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!
Job 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.
Qualifications
Requirements
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.
