About the job
Your Impact
Join our dynamic Data and ML Platform team as a Senior Staff DataOps Engineer. Your role will be pivotal in defining our data platform strategy and architecture, facilitating enterprise-scale solutions that propel machine learning initiatives, foster engineering excellence, and unveil critical business insights.
As part of the tech team at Doctolib, you will contribute to the development of innovative products and features that enhance the daily experiences of care teams and patients.
What You’ll Build
Your key responsibilities will include:
- Crafting and executing enterprise-scale data infrastructure strategies, performing comprehensive impact and cost analyses for significant technical decisions, and establishing architectural standards across the organization.
- Creating and optimizing intricate, multi-region data pipelines capable of processing petabyte-scale datasets, ensuring 99.9% reliability, and implementing advanced monitoring and alerting systems.
- Spearheading cost analysis initiatives, pinpointing optimization opportunities within our data stack, and executing solutions that minimize infrastructure costs while enhancing performance and reliability.
- Offering technical leadership to data engineers and cross-functional teams, conducting architecture reviews, and promoting the adoption of best practices in DataOps, security, and governance.
- Assessing emerging technologies, performing proof-of-concept for new data tools and platforms, and guiding the technical roadmap for data infrastructure modernization.
What You’ll Bring
If you feel your skill set aligns with this role, we encourage you to apply even if you don’t meet every qualification outlined below:
- 7+ years of experience in a similar role, such as Staff Data Platform Engineer, Staff DataOps, or Staff Site Reliability Engineer, with a proven track record in architecting and scaling robust data platforms.
- In-depth experience with Google Cloud Platform, and proficiency in Kubernetes & Terraform for automated deployments, alongside expertise in network and IAM security best practices.
- Strong technical skills in orchestrating data pipelines using Airflow or Dagster, deploying applications to the cloud, and leveraging modern data tools effectively.

