Accelerator - Data & AI Engineering - Paris
Role details
Job location
Tech stack
Job description
· Design and implement end-to-end data pipelines that transform raw data into valuable insights, ensuring scalability and reliability in cloud environments
· Develop and optimize data models with a focus on query performance and efficient workloads
· Collaborate with cross-functional teams to translate business requirements into technical solutions, defining clear interface contracts between data products and applications
· Ensure data quality, standardization, observability, and governance across systems, aligning with industry compliance standards and data privacy requirements
· Automate data ingestion processes and monitoring systems to track operational KPIs, troubleshoot issues, and maintain pipeline health
· Build and maintain ETL processes and machine learning workflows, providing clean AI-ready data for downstream applications
· Contribute to MLOps/AIOps practices including model deployment pipelines, model performance monitoring, and reproducibility of ML experiments in production environments
· Actively contribute to transversal data engineering best practices, including design patterns, CI/CD integrations, containerized deployments, and participate in peer review of code and technical documentation standards
· Provide technical guidance to team members, ensuring adherence to coding standards and best practices
· Coordinate project timelines and deliverables with cross-functional teams to ensure alignment with business goals
Requirements
Experience:
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Strong track record in designing and implementing data pipelines and data warehouse solutions in cloud environments
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Hands-on experience with data modeling, ETL/ELT processes, and pipeline orchestration in production settings
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Background working in cross-functional teams, translating business needs into technical solutions
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Experience working within compliance (e.g.: quality, regulatory, data privacy, GxP) and cybersecurity requirements
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Experience in the healthcare industry is a strong plus
Technical Skills:
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Strong proficiency in SQL and data warehousing platforms (Snowflake preferred)
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Hands-on experience with ETL tools (IICS or equivalent), data transformation tools (dbt preferred), and Python for data processing
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Strong experience with cloud platforms (AWS preferred) including orchestration frameworks (Airflow preferred)
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Good knowledge of CI/CD practices (GitHub Actions preferred), version control (Git), and infrastructure as code principles (Terraform preferred)
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Experience in designing and implementing engineering patterns and technical standards
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Good knowledge of logging and monitoring tools such as Datadog, Grafana
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Familiarity with containerization (Docker) and container orchestration (Kubernetes/Openshift a plus)
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Possessing relevant cloud certifications (AWS, Snowflake, IICS) is a plus
Soft Skills:
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Collaborative mindset with strong problem-solving abilities
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Self-motivated and able to take initiative in a fast-paced environment
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Effective communication skills to work with both technical and business stakeholders, with the ability to articulate insights and influence decision-making
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AI-assisted engineering as a default: AI tooling is a default expectation, not an experiment. Use it across coding, code review, documentation, and operations. We track adoption.
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Here for the patients: quiet ego, loud standards. Don't fight to be right, fight to deliver for the patients who count on us. Be ruthless on quality, never rude about it.
Education: Master's Degree or equivalent in Computer Science, Engineering, or relevant field
Languages: English, French