Top Data Engineer Jobs in Paris

Build and maintain cloud-based media data pipelines and user-facing data products. Design, deploy and govern BigQuery/DBT pipelines, APIs and dashboards, ensure data quality/security, manage vendors, and drive product-oriented delivery and adoption.

Own and scale Datadog’s batch analytics data platform, supporting extensive Spark, PySpark, Ray, and Python workloads. Build AI-driven migration automation, agentic tooling, engine-agnostic batch execution, reliability improvements, and Spark pod autosizing. Design high-performance distributed systems, improve developer infrastructure, and manage execution-model tradeoffs across large-scale workloads. Collaborate with internal data engineering and science teams while using AI coding tools to enhance development workflows.

Own and scale HyLight’s client insights platform, processing terabytes of LiDAR, imagery, and methane data into customer-facing web applications and dashboards. Improve reliability, speed, data quality, observability, scalability, and cost efficiency across cloud and potential in-house infrastructure. Work end to end from backend and data pipelines to user interfaces, partner with operations and embedded teams, and apply AI tools to workflows such as data QA and incident triage.

Build and maintain data models, ingestion pipelines, and analytics infrastructure using Python, SQL, Snowpark, PySpark, Dagster, Snowflake, and AWS. Own data topics end to end, collaborate with business stakeholders to define metrics, monitor pipeline operations, investigate data issues, and ensure code quality through testing and documentation. The role also supports internal AI tools and requires working onsite three days per week.

Support ML engineers by improving the MLOps platform, implementing data science algorithms in client applications, collaborating on infrastructure, and deploying algorithms safely and scalably. The role involves Python backend development, data stores, CI/CD, Kubernetes infrastructure, monitoring, and production support. Candidates should be pursuing a Master's degree in Engineering and seeking an end-of-study internship.

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 operate Python backend services and AI-agent pipelines that crawl, process, validate, and index legal documents. Design retry-safe workflows, APIs, database schemas, migrations, lineage tracking, evaluation gates, and observability. Collaborate with legal experts and engineering teams to onboard new data sources, improve extraction quality, control processing costs, and resolve production failures.

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.

Design, operate, and scale highly available Kubernetes platforms across multi-region environments. Improve reliability, resilience, performance, observability, and SRE practices across Data & WebCloud services. Automate infrastructure and deployments using Terraform, Ansible, GitOps, and CI/CD. Troubleshoot critical incidents, conduct root-cause analysis, and collaborate with engineering, database, networking, and observability teams on distributed architectures and production reliability.

Research data-centric methods that improve humanoid robot learning. Responsibilities include designing multimodal datasets, data curation and selection tools, training-stage data composition, controlled ablations, scaling studies, and real-robot evaluation. The role connects research findings to data collection strategy and reproducible training workflows, collaborating with AI, robotics, infrastructure, and data engineering teams.

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.

Designs, builds, deploys, and supports production AI, data, and software solutions for clients. Responsibilities include writing production code, architecting systems, building data pipelines and AI workflows, implementing testing and monitoring, driving user adoption, and guiding engineers. The role also involves client engagement, technical scoping, stakeholder communication, project estimation, candidate interviewing, and development of reusable internal AI capabilities.

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 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.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and improve data platform tools, standards, and infrastructure that enable engineering teams to own and operate data pipelines. Develop AI tooling, support batch and event-driven architectures using Airflow, dbt, and Kafka, establish engineering best practices, and help teams adopt platform capabilities across France and Brazil.

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.

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.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

Designs and maintains scalable cloud data pipelines, data models, ETL processes, machine learning workflows, and AI-ready datasets. The role focuses on data quality, governance, observability, monitoring, CI/CD, containerized deployments, and technical standards. It collaborates with business and technical stakeholders to translate requirements into solutions while applying generative AI technologies and supporting Sanofi’s commercial digital transformation.

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.