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Staff DataOps Engineer (x/f/m)

Оценка ИИ

Отличная позиция в одной из ведущих health-tech компаний Европы с сильной инженерной культурой и современным стеком. Предлагается расширенный социальный пакет, включая поддержку родителей и гибкий график, хотя отсутствие явного указания зарплаты немного снижает прозрачность.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль уровня Staff требует не только глубоких технических знаний GCP, Kubernetes и Dagster, но и способности принимать стратегические архитектурные решения на уровне всей компании. Процесс отбора включает сложные этапы System Design и Case Study, что подтверждает высокий порог входа.

Анализ зарплаты

Медиана95 000 €
Рынок85 000 € – 120 000 €
Оценка ИИ

Указанная роль Staff уровня в Париже предполагает компенсацию выше среднего по рынку. Doctolib известен конкурентными зарплатами, которые обычно находятся в верхнем квартиле для опытных инженеров в Франции.

Сопроводительное письмо

I am writing to express my strong interest in the Staff DataOps Engineer position at Doctolib. With over 7 years of experience in architecting and scaling robust data platforms, I have developed a deep expertise in Google Cloud Platform, Kubernetes, and Terraform, which aligns perfectly with your current tech stack and mission to drive a data-driven transformation.

In my previous roles, I have successfully led the design of multi-region data pipelines handling petabyte-scale datasets and implemented advanced monitoring systems to ensure high reliability. I am particularly drawn to Doctolib's commitment to ethical AI and engineering excellence. My background in orchestrating complex workflows with Dagster and optimizing BigQuery performance, combined with a strong focus on FinOps and security, positions me to make an immediate impact on your Data and ML Platform team.

I am excited about the opportunity to contribute to a platform that improves the lives of care teams and patients. Thank you for considering my application. I look forward to the possibility of discussing how my technical leadership and architectural experience can support Doctolib's ambitious roadmap.

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Составьте идеальное письмо к вакансии с ИИ-агентом

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Откликнитесь в doctolib уже сейчас

Присоединяйтесь к Doctolib, чтобы проектировать масштабируемую инфраструктуру данных для миллионов пользователей и формировать будущее цифрового здравоохранения в Европе!

Описание вакансии

Your Impact

We are looking for a Staff DataOps Engineer to join the Data and ML Platform team.

Your mission will be to shape our data platform strategy and architecture, driving enterprise-scale solutions that accelerate machine learning initiatives, enable engineering excellence, and unlock business insights. You will work in a team at the heart of Doctolib's data-driven transformation, enabling innovation through robust, scalable data infrastructure that empowers engineers, AI teams, and business stakeholders across the organization.

Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients.

What you'll build

Your responsibilities include but are not limited to:

  • Design and implement enterprise-scale data infrastructure strategies, conducting thorough impact and cost analysis for major technical decisions, and establishing architectural standards across the organization
  • Build and optimize complex, multi-region data pipelines handling petabyte-scale datasets, ensuring 99.9% reliability and implementing advanced monitoring and alerting systems
  • Lead cost analysis initiatives, identify optimization opportunities across our data stack, and implement solutions that reduce infrastructure spend while improving performance and reliability
  • Provide technical guidance to data engineers and cross-functional teams, conduct architecture reviews, and drive adoption of best practices in DataOps, security, and governance
  • Evaluate emerging technologies, conduct proof-of-concepts for new data tools and platforms, and lead the technical roadmap for data infrastructure modernization

What you'll bring

Before you read on: if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply.

You'll be a great fit if you:

  • You have 7+ years of experience after graduation as a Staff Data Platform Engineer, Staff DataOps, Staff Site Reliability Engineer, or in a similar role, with a history of architecting and scaling robust data platforms
  • You have extensive experience with Google Cloud Platform and a command of Kubernetes & Terraform for automated deployments, and you are an authority on implementing network and IAM security best practices
  • You have deep technical proficiency in orchestrating data pipelines using Airflow or Dagster, deploying applications to the cloud, and leveraging modern data warehouses such as BigQuery
  • You are highly skilled in programming with Python, and have a solid understanding of software development principles
  • You are an excellent troubleshooter who excels at diagnosing and fixing data infrastructure and identifying performance bottlenecks, and a strong communicator who can articulate complex technical concepts to both technical and non-technical audiences

It would be fantastic if you:

  • Have hands-on experience building and deploying APIs, with a strong preference for frameworks like FastAPI, and are skilled at designing APIs that provide reliable and efficient access to data
  • Have actively applied data governance principles to manage data quality, security, and compliance, and understand how to implement controls that protect sensitive information
  • Are experienced with CI/CD tools and methodologies for data-related projects, and can build automated pipelines that streamline development, testing, and deployment
  • Have practical experience with cloud cost optimization and FinOps principles, actively experimenting with cost-reduction strategies, and are comfortable analyzing infrastructure spending and optimizing resource allocation

Life at Doctolib Tech

  • Our solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.
  • Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.
  • We leverage AI ethically across our products to empower patients and health professionals. Discover our AI visionhere.

Want to learn more about our tech culture and environment? Visit theDoctolib Tech site.

What we offer

  • Free comprehensive health insurance for you and your children
  • 25 days of paid vacation per year, plus up to 14 days of RTT
  • Free mental health and coaching services through our partner Moka.care
  • Work from abroad for up to 10 days per year thanks to our flexibility days policy
  • Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib
  • A subsidy from the work council to refund part of the membership to a sport club or a creative class
  • 50% reimbursement of your public transport subscription
  • Parent Care Program: receive one additional month of leave on top of the legal parental leave
  • For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
  • Relocation support in case of international mobility
  • Access to the best AI tools for coding, development and dedicated training

Our interview process

  • Recruiter Interview
  • Case Study
  • System Design Interview
  • Behavioral interview with Hiring Manager
  • At least one reference check

We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on ourcandidate experience page.

Job details

  • Permanent position
  • Tech stack: Google Cloud Platform, Kubernetes, Terraform, Dagster, BigQuery, dbt
  • Full-time
  • Paris
  • Start date: as soon as possible

We welcome everyone

At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.

The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, or disability.

To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process.

Join us in building the healthcare we all dream of!

Your data privacy

All information provided is processed by Doctolib for application management. For data processing details, click here:France. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.

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Создайте идеальное резюме с помощью ИИ-агента

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Навыки

  • Python
  • Terraform
  • dbt
  • Kubernetes
  • CI/CD
  • Google Cloud Platform
  • BigQuery
  • FinOps
  • Airflow
  • Data Governance
  • Dagster
  • FastAPI

Возможные вопросы на собеседовании

Проверка опыта проектирования систем на уровне Staff.

Опишите ваш опыт проектирования мультирегиональной архитектуры данных для петабайтных объемов. С какими основными сложностями вы столкнулись при обеспечении надежности 99.9%?

Оценка навыков оптимизации затрат, что указано в обязанностях.

Какие стратегии FinOps вы применяли для оптимизации расходов в Google Cloud Platform, особенно в контексте использования BigQuery и Kubernetes?

Проверка владения современными инструментами оркестрации.

Почему для масштабируемых платформ данных вы бы предпочли Dagster вместо Airflow (или наоборот)? Приведите примеры из практики.

Оценка лидерских качеств и влияния на процессы.

Как вы внедряли стандарты DataOps и лучшие практики безопасности (IAM, сетевая безопасность) в кросс-функциональных командах?

Проверка навыков решения критических проблем.

Расскажите о самом сложном инциденте в инфраструктуре данных, который вы расследовали. Как вы выявили первопричину и какие превентивные меры внедрили?

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