Data Engineer Python - Freelance (H/F)

France | Nov. 5, 2026

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Company: Collective.work

Country: France

Type: Onsite

Employment: Full-time

Description: Daily rate (TJM): 500 As part of a project to make data available for analysis, testing or development purposes, you contribute to the design and industrialization of data preparation and anonymization processing. You ensure their quality, their traceability and their execution at scale. The objectives and criteria for anonymization are defined with the competent contacts, in particular the DPO and the Compliance and Security teams. Pseudonymization should not be equated with anonymization.Main missions * Design, develop and maintain data pipelines in Python. *    Process large volumes using Dask and Polars, evaluating their relevance according to use cases. *    Implement the transformations defined as part of the project: deletion or transformation of fields, generalization, aggregation or other techniques retained. *    Contribute to the detection of directly or indirectly identifying data, in coordination with the experts concerned. * Develop automated controls: schema, quality, consistency, completeness and regression detection. *    Measure processing performance and optimize their resource consumption. *   Ensure traceability of processing, code versions and datasets produced. *    Document transformation rules, known limits and control results. *    Participate in code reviews, testing and continuous integration. *    Collaborate with the business, Data, Security, Compliance and Data Protection teams. Technical skills required *    Very good command of Python and the associated data ecosystem. *    Practical experience with Polars and/or Dask; ability to compare their uses and performances. *    Mastery of tabular data processing and common formats: Parquet, CSV, JSON. *   Practice designing reliable and repeatable pipelines. *    Proficiency in automated testing, data quality control and versioning with Git. *    Knowledge of industrialized deployment and execution environments: containers, schedulers or distributed platforms depending on the project environment. *    Sensitivity to the issues of data protection, minimization of access and security of environments.

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