Top Data Engineer Jobs in Paris

Build and ship end-to-end features for Akeneo Supplier Data Manager across data-processing pipelines, backend services, and TypeScript/React frontend systems. Help process large, messy supplier catalogues using deterministic workflows and LLM-based extraction and classification. Own features through production monitoring and support, participate in customer discovery, collaborate across the Product Cloud, and develop engineering skills through pairing, code review, and mentorship.

Design, build, and operate Akeneo’s Supplier Data Manager at enterprise scale. Develop reliable data-processing pipelines for messy supplier catalogues, including deterministic and LLM-powered extraction and classification. Create collaboration and governance workflows, ensure production observability and operational excellence, participate in customer discovery, and collaborate across teams. The role requires strong software engineering fundamentals, architectural thinking, debugging ability, end-to-end ownership, and clear communication.

Own the technical transformation of Finance by building data platforms, pipelines, models, dashboards, and internal tools. Migrate spreadsheet workflows to reliable code-based solutions, establish data quality and governance standards, partner with Finance and Data teams, and identify AI and automation opportunities. Support Finance users through documentation, training, and ongoing collaboration.

Build, operate, and improve Alan’s data platform and production data infrastructure. Responsibilities include developing data pipelines, enabling secure warehouse access, strengthening data security and governance, standardizing data practices, supporting engineers and analysts, and scaling infrastructure for international expansion. The role requires collaboration across technical and non-technical teams, with a focus on reliable, secure, and developer-friendly data systems.

Build and maintain large-scale data pipelines, transform raw location and behavioral data into reliable features, and create datasets for products, analytics, and machine learning. Explore complex data, develop metrics and visualizations, monitor quality, investigate anomalies, and translate exploratory analyses into production systems. Collaborate with product, engineering, and data teams to improve data infrastructure and product intelligence.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Build, maintain, and improve Alan’s data platform and infrastructure. Develop reliable data pipelines, support secure data warehouse access, strengthen data governance, and establish standardized data practices. Partner with technical and non-technical teams to deliver trusted data solutions, improve developer experience, and support international expansion. The role involves operating production infrastructure using orchestration, cloud warehouses, infrastructure as code, Python, SQL, and related tools.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Owns processing and technical quality of MRI data from raw acquisitions through quality-controlled datasets. Builds and maintains MRI processing and QA/QC pipelines, monitors acquisitions across imaging centers, investigates data-quality issues, and improves collection processes. Collaborates with MRI physicists, clinical research associates, and model developers to adapt pipelines and validate new sequences and protocols across sites.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Build and improve a scalable data platform, including ingestion, transformation, warehousing, orchestration, governance, observability, and CI/CD. Audit the existing stack, establish data engineering standards, improve pipeline reliability and cost control, and deliver trusted datasets for product analytics, data features, and ML/AI use cases. Collaborate with Product, business teams, and company leadership on architecture decisions and strategic data initiatives.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Own end-to-end scientific data pipelines from ingestion through trusted, model-ready datasets. Model compounds, structures, assays, predictions, and experimental results while ensuring data quality, lineage, governance, and observability. Partner with ML engineers, data scientists, and researchers to build scalable solutions, shape data architecture, develop self-service and API capabilities, and mentor junior engineers.

Design, build, operate, and optimize large-scale Spark data pipelines for data lake ingestion involving hundreds of terabytes. The role uses Databricks, Snowflake or AWS Redshift, AWS cloud services, and Delta Lake or Apache Iceberg. Responsibilities include performance and cost optimization, lakehouse architecture, secure data management, governance, compliance, observability, and infrastructure practices. The engineer will collaborate within Odaseva’s R&D team to develop secure, scalable data platform capabilities.

Develop and optimize large language models and NLP capabilities for decision support, mission planning, workflow automation, and situational awareness. Responsibilities include data preprocessing, model training and fine-tuning, RAG and agentic system development, evaluation, deployment, monitoring, documentation, and collaboration with engineering and product teams. The role requires strong Python, deep learning, LLMOps, cloud, and MLOps expertise, plus knowledge of responsible AI and bias mitigation.

Lead design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, build self-service operational tooling, drive reusable services, mentor engineers, influence cross-team architecture, and participate in on-call to improve platform reliability.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

As a DevOps Engineer, you will ensure the reliability, scalability, and performance of database platforms, automate operations, and support Kubernetes adoption, while collaborating with engineering and data teams.

Own electrical engineering and project delivery for energy infrastructure serving data centres and industrial customers. Responsibilities include managing development programmes and budgets, grid connection processes, electrical design, consultant and contractor coordination, design reviews, risk management, procurement, commissioning, energisation and handover. The role requires MV/HV infrastructure experience, GB or European grid connection expertise, multidisciplinary design coordination and accountability across first-of-a-kind power projects.

Design and build production data pipelines for place and location datasets. Work with large, noisy geospatial data, improve dataset quality and observability, define data-quality metrics, prototype in Python/SQL notebooks, integrate experiments with backend systems, and run offline evaluations and A/B tests.