Data Engineer

Perceptive Inc.

. Build scalable, cloud-native data pipelines for batch and streaming workloads .

Posted 9/15/2026full-timeRemote • FranceMid-LevelSenior💰 €47,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building scalable, cloud-native data pipelines and implementing data ingestion frameworks for diverse data types. Proficient in developing APIs, ensuring data quality, and supporting compliance with regulations such as FDA/GxP and GDPR.

Highest-signal resume keywords

Cloud-Native Data Platforms (Azure, AWS)Data Pipeline Orchestration (Airflow, Prefect, Dagster)Streaming Platforms (Kafka, Kinesis, Pub/Sub)SQL ProficiencyScalable Data Processing Frameworks (Spark, Flink, Beam)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentAPI DevelopmentData Quality FrameworksData ModellingMetadata-Driven IngestionData Processing OptimizationData GovernanceData Privacy ComplianceReal-Time Data ProcessingFeature Store Development

Soft Skills

Good Communication SkillsCollaboration Across Teams

Tools & Technologies

CI/CDDevOpsELT/ETL ToolsAnalytics Tools (Power BI, Tableau, Looker)Distributed Data Processing

Industry Keywords

FDA/GxP ComplianceGDPR ComplianceHIPAA ComplianceData Lake SystemsData Warehousing

Tech Stack

Tools & technologies

AirflowAWSAzureCloudETLGoogle Cloud PlatformKafkaNoSQLPythonSparkSQLTableau

About the role

Key responsibilities & impact

Build scalable, cloud-native data pipelines for batch and streaming workloads
Support implementation of data ingestion frameworks for structured, semi-structured, and unstructured data
Develop APIs and integration services for data exchange between internal systems
Support platform scalability and reliability across global operations while collaborating with Senior Engineers
Support implementation of database and storage solutions across relational, NoSQL, and data lake systems
Develop metadata-driven ingestion and transformation frameworks
Assist in ensuring data schemas support analytics, AI/ML, and business applications
Implement data quality frameworks, validation rules, and automated checks
Build reusable transformation components to standardise data processing
Support data lineage, cataloguing, and governance capabilities under guidance
Ensure data privacy, protection, and regulatory compliance
Develop high-throughput distributed data processing solutions
Optimise data pipelines for performance, cost efficiency, and resilience
Contribute to observability and monitoring for pipeline health, data drift, and SLA compliance
Build caching, partitioning, and indexing strategies to improve query performance
Enable data scientists with curated datasets and feature pipelines
Develop real-time or batch-oriented feature stores
Integrate data workflows with MLOps and model deployment systems under guidance
Build visualisation-ready datasets for BI tools and dashboards
Apply security practices including role-based access control, encryption, and secure data-sharing patterns
Support compliance with FDA/GxP, GDPR, HIPAA, and other regulations
Develop audit trails, data retention, and disaster recovery solutions
Stay informed on emerging data engineering, AI, and cloud technologies
Share insights and drive continuous improvement across data engineering practices

Requirements

What you’ll need

Working knowledge of cloud-native data platforms and serverless architectures (Azure, AWS)
Good understanding of CI/CD and DevOps concepts
Understanding of data modelling concepts
Familiarity with API patterns
Knowledge of streaming platforms (Kafka, Kinesis, Pub/Sub, Event Hubs)
Strong SQL skills
Good communication skills with ability to work across technical and business teams
Experience in data pipeline orchestration (e.g., Airflow, Prefect, Dagster)
Experience with scalable data processing frameworks (Spark, Flink, Beam, Databricks)
Experience working in data engineering or related technical roles
Exposure to cloud-native data platforms (Azure, AWS, or GCP)
Exposure to modern ELT/ETL tools and distributed data processing
Good experience in Python, SQL, and optionally 3GL technologies
Exposure to modern data warehousing and lakehouse platforms
Exposure to analytics tools (Power BI, Tableau, Looker) beneficial
Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or related discipline
English: Fluent

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Perceptive Inc. Website LinkedIn All Job Openings 1001 - 5000 employees 🏥 Healthcare 💼 Consulting 📦 Logistics Healthcare
Consulting
Logistics Perceptive Inc. is a leading provider of scientific and data-driven solutions for the bio-pharmaceutical industry, focusing on oncology, central nervous system (CNS) therapeutic areas, and musculoskeletal imaging. With expertise in drug discovery, preclinical and early clinical imaging, and radiochemistry, Perceptive offers services that identify promising candidate compounds and mitigate risks through premier imaging services. It also provides comprehensive eClinical solutions, including Randomization and Trial Supply Management (RTSM), aimed at optimizing clinical trial processes. Trusted for its high-quality data, Perceptive serves as a partner in advancing clinical trials and bringing essential treatments to patients faster, backed by over 50 years of experience and a team of dedicated experts. Perceptive's global presence ensures seamless delivery of Discovery, eClinical, and Imaging services to support worldwide clinical trials. Data Engineer Job not on LinkedIn 🔥 28 minutes ago 🇫🇷 France – Remote 💵 €47.3k / year ⏰ Full Time 🟡 Mid-level 🟠 Senior 🚰 Data Engineer 👻 Ghost score 0% Airflow AWS Azure Cloud ETL Google Cloud Platform Kafka NoSQL Python Spark SQL Tableau Apply Now Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Build scalable, cloud-native data pipelines for batch and streaming workloads
Support implementation of data ingestion frameworks for structured, semi-structured, and unstructured data
Develop APIs and integration services for data exchange between internal systems
Support platform scalability and reliability across global operations while collaborating with Senior Engineers
Support implementation of database and storage solutions across relational, NoSQL, and data lake systems
Develop metadata-driven ingestion and transformation frameworks
Assist in ensuring data schemas support analytics, AI/ML, and business applications
Implement data quality frameworks, validation rules, and automated checks
Build reusable transformation components to standardise data processing
Support data lineage, cataloguing, and governance capabilities under guidance
Ensure data privacy, protection, and regulatory compliance
Develop high-throughput distributed data processing solutions
Optimise data pipelines for performance, cost efficiency, and resilience
Contribute to observability and monitoring for pipeline health, data drift, and SLA compliance
Build caching, partitioning, and indexing strategies to improve query performance
Enable data scientists with curated datasets and feature pipelines
Develop real-time or batch-oriented feature stores
Integrate data workflows with MLOps and model deployment systems under guidance
Build visualisation-ready datasets for BI tools and dashboards
Apply security practices including role-based access control, encryption, and secure data-sharing patterns
Support compliance with FDA/GxP, GDPR, HIPAA, and other regulations
Develop audit trails, data retention, and disaster recovery solutions
Stay informed on emerging data engineering, AI, and cloud technologies
Share insights and drive continuous improvement across data engineering practices 🎯 Requirements
Working knowledge of cloud-native data platforms and serverless architectures (Azure, AWS)
Good understanding of CI/CD and DevOps concepts
Understanding of data modelling concepts
Familiarity with API patterns
Knowledge of streaming platforms (Kafka, Kinesis, Pub/Sub, Event Hubs)
Strong SQL skills
Good communication skills with ability to work across technical and business teams
Experience in data pipeline orchestration (e.g., Airflow, Prefect, Dagster)
Experience with scalable data processing frameworks (Spark, Flink, Beam, Databricks)
Experience working in data engineering or related technical roles
Exposure to cloud-native data platforms (Azure, AWS, or GCP)
Exposure to modern ELT/ETL tools and distributed data processing
Good experience in Python, SQL, and optionally 3GL technologies
Exposure to modern data warehousing and lakehouse platforms
Exposure to analytics tools (Power BI, Tableau, Looker) beneficial
Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or related discipline
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