Top Data Engineer Jobs in Paris

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

Designs, builds, and maintains scalable data pipelines and products using Azure and Databricks. Responsibilities include ELT/ETL development, workflow orchestration, data quality and observability, testing, monitoring, CI/CD, performance and cost optimization, production troubleshooting, documentation, and stakeholder collaboration. The role requires strong Python and SQL skills, modern data engineering experience, data governance knowledge, and French and English proficiency.

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.

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.

Own Filigran’s Enterprise Data Platform end to end, including ingestion pipelines, dbt-based data modeling, quality testing, observability, governance, and Metabase dashboards. Maintain and troubleshoot Airbyte and Airflow pipelines, develop trusted KPI models, and enable self-service analytics across business teams. Collaborate cross-functionally, improve platform architecture, and independently deliver reliable data products in a remote, async-first environment. Bonus initiatives include AI agents, LLM integrations, semantic layers, and proactive data alerting.

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.

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.

The Software Engineer will enhance the data ingestion pipeline, collaborating with scientists for model training and data optimization, while managing cloud infrastructure.

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.

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.

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.

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.

Build and maintain analytics-ready data models using dbt and SQL, implement data quality checks, and develop official Sigma dashboards. Help establish shared metric definitions, documentation, and data governance across the merged group. Automate data workflows using AI coding tools and agents, support AI-powered self-service analytics, and bridge technical data work with business users.

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.