Paris-Based Staff DataOps Engineer — Data Platform Lead

Doctolib (France); France; On-site; EUR 90,000 - 120,000; Full time

Benefits offered by this job

Health insurance for you and family

30 days vacation + RTT

Mental health support

Relocation assistance

Lunch vouchers

Job summary

Doctolib Staff DataOps Engineer role in Paris sits at the heart of our Data & AI Platform. You will shape platform strategy, architect enterprise-scale data infrastructure, and drive ML initiatives while enabling engineering excellence and business insights across the organization.

You will lead multi-region pipelines, implement robust monitoring, ensure security governance, and guide cross-functional teams toward best practices in DataOps and cloud-native architectures.

Qualifications

• 7+ years of experience as a Staff Data Platform Engineer or similar role.
• Extensive experience with Google Cloud Platform and Terraform/Kubernetes for deployments.
• Proficiency in orchestrating data pipelines with Airflow or Dagster and using BigQuery.

Responsibilities

• Design and implement enterprise-scale data infrastructure strategies and standards.
• Build and optimize multi-region data pipelines with high reliability.
• Lead cost analysis and optimization across the data stack.
• Provide technical guidance to data engineers and cross-functional teams.
• Evaluate emerging data tools and drive the data infrastructure roadmap.

Skills

Python

DataOps

Cloud architecture

Data pipelines

Communication

Tools

Google Cloud Platform

Kubernetes

Terraform

Dagster

BigQuery

dbt

Airflow

Job description

Doctolib Staff DataOps Engineer role in Paris sits at the heart of our Data & AI Platform. You will shape platform strategy, architect enterprise-scale data infrastructure, and drive ML initiatives while enabling engineering excellence and business insights across the organization.

You will lead multi-region pipelines, implement robust monitoring, ensure security governance, and guide cross-functional teams toward best practices in DataOps and cloud-native architectures.