Top Data Engineer Jobs in Paris

Build and deploy an AI platform feature involving LLMs and agentic workflows for fraud detection or payment integrity. Responsibilities include researching approaches, designing solutions, developing and integrating features, evaluating precision, recall, and latency, and deploying systems to production with cross-functional teams.

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.

Build and scale distributed data ingestion services for Algolia’s Connectors Platform. Responsibilities include improving reliability, scalability, throughput, testing, observability, and performance; operating high-traffic production systems; and mentoring teammates. The role works with Go, Kubernetes, Terraform, and Google Cloud to support data connectivity, transformation, and AI enrichment pipelines.

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.

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.

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.

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.

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.

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.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Design and operate production data pipelines and standardized data models for a multi-tenant Azure SaaS platform serving container terminals. Build near-real-time and batch ELT pipelines from operational systems, develop Go-based connectors, implement data quality monitoring, and support source-to-curated transformations across PostgreSQL layers. Collaborate directly with terminal operations staff while owning architectural decisions and platform scalability.

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

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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.

Maintain and improve Doctolib’s cloud-native Data and AI Platform, including the Data Product Controller and Dagster orchestrator. Enable autonomous data product deployment through CI/CD and infrastructure as code, while monitoring reliability, performance, costs, data quality, lineage, and observability. Collaborate with data engineers, data scientists, and product stakeholders to resolve infrastructure issues and improve platform scalability, architecture, and developer experience.

Design, build, and operate batch and streaming data pipelines; move production data into data lakes; ensure dataset quality, freshness, and observability; optimize queries and processing for correctness, latency, and cost; prototype and integrate production experiments; build reusable derived datasets.

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