Top Data Engineer Jobs in Paris

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.

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.

Design, build and maintain scalable data models, ETL pipelines, and data access layers. Collaborate with product and data science teams to enable analytics and dashboards, participate in agile processes, code reviews, deployment, and monitoring to deliver production-quality data solutions.

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

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.

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 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.

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.

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 and develop UWB, radar and camera-based data fusion algorithms and sensing features. Perform DSP analysis, feature extraction, algorithm optimization, AI model integration, end-to-end integration and validation, and cross-team collaboration for automotive sensing products.

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.

The MES Integration Engineer will manage the deployment and configuration of a Manufacturing Execution System for quantum chip fabrication, focusing on integrating fabrication processes and data management in an R&D environment.

Design, build, and maintain production ELT pipelines for high-volume streaming spatial and telemetry data. Architect Databricks/AWS schemas for ultra-low latency and zero-data-loss. Develop and optimize a central semantic layer using Cube.js for reliable data consumption. Ensure LLM-agent friendly semantic modeling, automation of pipeline unit tests, and package integration blueprints for Professional Services.

Design, build, and operate data infrastructure to process massive event volumes into reliable, low-latency analytics and data products. Own real-time and batch services, platform tooling, observability, cost and performance optimization, on-call incident response, and cross-team stakeholder collaboration to enable analytics, ML, and reporting at scale.

Design, build, and maintain scalable cloud data pipelines, ETL/ELT and data warehouse solutions (Snowflake preferred). Ensure data quality and governance, support automated ingestion and monitoring, optimize data models for performance, implement CI/CD and infrastructure-as-code patterns, and collaborate with cross-functional teams to deliver production-ready AI-ready data and ML workflows.

Design, operate, and improve Deezer's data ingestion and platform infrastructure. Maintain and monitor pipelines, support Airflow rollout, schedule GCP jobs on Spark and BigQuery, collaborate with stakeholders, and implement CI/CD, monitoring, and FinOps improvements.

Responsible for data collection to support model training, finding new audio data sources, and operating the cloud infrastructure for ingestion pipelines.

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