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.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

As a Data Engineer, build and optimize high-performance data infrastructures for deep learning by managing large volumes of raw video data, ensuring efficient processing pipelines and data quality.

As the Data Engineer, you will design and maintain data pipelines, scale data infrastructure, ensure data quality, and collaborate with analysts to enhance decision-making capabilities.

As a Data Analyst, you will analyze data to drive insights, create reports and dashboards, and collaborate with teams to optimize product performance.

Owns the full lifecycle of pricing data products, including data pipeline ownership, architecture, production-quality coding, and ML system infrastructure. Collaborates with data scientists and cross-functional teams to deploy real-time pricing algorithms, scale ML infrastructure, optimize performance, and build enterprise data warehouses, marts, dashboards, and KPI metrics. The role also manages orchestration, cloud infrastructure, DevOps, data governance, GDPR compliance, and stakeholder communication.

Build and maintain GTM systems and data pipelines: design data models, develop internal FastAPI/React apps, lead an OpenAI Agent-based GTM AI layer, drive architecture and CI improvements, perform code reviews, and collaborate with Sales, RevOps, Data, and R&D to increase adoption and reliability.

Build and maintain production data pipelines for AI systems, from ingestion and data capture through monitoring. Ensure data quality, privacy, anonymization, consent, retention, and GDPR compliance. Provide datasets for model training, evaluation, and annotation workflows, while developing monitoring pipelines and dashboards for AI performance. Collaborate with data platform, data science, engineering, product, and business teams to optimize scalable cloud infrastructure and manage costs.

Six-month analytics engineering internship contributing to a data platform: implement an AI-ready context layer, build AI agents for analytics workflows, clean up legacy tables, collaborate with engineers and business teams, and document architecture and data practices.

Lead technical direction and hands-on implementation of semantic infrastructure for telemetry interoperability. Build scalable systems bridging OpenTelemetry, cloud and customer data models; define standards, mentor engineers, run on-call rotations, and drive cross-team platform initiatives for schema evolution and AI-enabled observability features.

Build and operate scalable ETL/ELT pipelines to ingest, transform, and model data across Palantir Foundry and Databricks. Ensure data quality, lineage, observability, and access controls. Collaborate with analysts and product teams to translate requirements into reliable, documented datasets and establish standards, monitoring, and automation for production data flows.

Build and operate Vibe's petabyte-scale data platform: storage, compute, orchestration, and OLAP reporting. Optimize pipelines for cost and latency, run on-call and incident response, write alerts/runbooks, and partner cross-functionally to deliver reliable, performant data services for product, ML, and finance teams.

Own and operate production reporting pipelines: implement schema changes, move and transform tables, ensure accurate advertiser reporting, troubleshoot discrepancies, and collaborate with Data Platform to flag scaling or data-quality risks.

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 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.

Build and maintain Databricks-based Lakehouse pipelines (batch and near-real-time) using PySpark. Design Delta Lake schemas, ensure pipeline reliability and cost efficiency, write tests, and collaborate with product and data teams to enable data enrichment and serving for internal and client products.

As a Data Engineer, you will develop data pipelines, support data scientists, deploy cloud infrastructure, and collaborate within the data community.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

Join as the first data-hire to grow the Data Warehouse: design and build ETL/ELT pipelines, model payment and product data, automate processes with Python, create dashboards, and implement data validation tests while gathering requirements from business teams.

Design, build, and maintain scalable data pipelines. Optimize workflows on Databricks and Snowflake, ensuring data security and governance while preparing for AI/ML use cases.

Design, build, and operate ingestion, curation, versioning, and delivery pipelines that convert terabytes of raw, unstructured multimodal field and public data into training-ready datasets. Align multimodal streams, define labeling requirements, enable reproducible dataset lineage, optimize storage and I/O for model training, and provide tooling for dataset construction and loading.

The Senior Python Developer will build and enhance data platforms for quantitative trading, ensuring code quality and performance while collaborating with teams.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.