Senior Data Engineer Python/GCP (x/f/m)

Benefits

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Senior Data Engineer Python/GCP (x/f/m) Paris, Paris, France Your Impact We are looking for a Senior Data Engineer to join the AI Team working on our AI Medical Companion . Your mission will be to build and optimize the data foundations that power safe, scalable, and impactful AI models. You will work on data infrastructure for LLM, VLM, and RAG-based systems , ensuring our engineers and data scientists can train, evaluate, and deploy AI models efficiently on high-quality, well-structured, and compliant data. Your work will directly support health professionals in delivering better care while improving their work-life balance, ultimately impacting 80 million patients and 400,000 healthcare professionals across Europe. 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 do Your responsibilities include but are not limited to: - Design, build, and maintain scalable data pipelines on Google Cloud Platform (GCP) for AI and machine learning use cases - Implement data ingestion and transformation frameworks that power Retrieval systems and training datasets for LLMs and multimodal models - Architect and manage NoSQL and Vector Databases to store and retrieve embeddings, documents, and model inputs efficiently - Collaborate with ML and platform teams to define data schemas, partitioning strategies, and governance rules that ensure privacy, scalability, and reliability - Integrate unstructured and structured data sources (text, speech, image, documents, metadata) into unified data models ready for AI consumption - Optimize performance and cost of data pipelines

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