Data Reliability Engineer

This is a fully remote position, open to applicants in France.

📋 Description

• Design and manage data pipelines for the ingestion of application events and databases, their transformation, and subsequent export.

• Develop pipeline tools in collaboration with SRE, focusing on infrastructure as code, deployment, CI/CD, secrets management, database operations, and incident responses.

• Oversee data quality and pipeline performance using Grafana dashboards, alerts, metrics, and log analysis.

• Establish and maintain data contracts along with automated data-quality tests to ensure freshness, completeness, and referential integrity.

• Provide secure data exports to external partners and institutions.

• Set up and manage dedicated PostgreSQL Research Spaces, implementing study-specific access control and data scoping.

• Streamline dbt projects to ensure that Data Analyst models are centralized, parallelized, and effectively utilized across various targets.

• Assess requests, evaluate their feasibility, deliver solutions, and document the processes.

• Work alongside product squads on emitted data and contribute to TypeScript implementations as necessary.

• Engage in code reviews, technical design discussions, and the continuous enhancement of engineering standards.

• Take ownership of the comprehensive data platform encompassing ingestion, transformation, exports, and supporting infrastructure.

• Provide support to clinical research, medical, and operational stakeholders.

• Participate in various hiring interviews including screening, manager-fit, technical case study, team-fit, culture-fit, and strategic-fit evaluations.

⛳️ Requirements

• Proficient in production-grade Python, including testing, typing, code quality, reviews, and CI practices.

• Experience in building and managing data pipelines and orchestration using tools such as Prefect, Airflow, Dagster, or similar.

• Familiarity with Kafka, S3, and SFTP.

• Advanced knowledge of PostgreSQL SQL, including indexing, query plans, and incremental loading strategies.

• Expertise in dbt, including macros, Jinja, multi-target projects, incremental strategies, testing, packages, and CI integration.

• Proficient in Terraform, Helm/Kubernetes, containers, CI/CD processes, monitoring, and alerting systems.

• Experience with Grafana or similar observability tools.

• Ability to read and contribute to TypeScript codebases.

• Understanding of back-end API architecture including REST design, versioning, authentication, pagination, and contracts.

• Strong communication skills.

• Capability to translate operational or scientific requirements into technical specifications and evaluate feasibility/compliance.

• Comfortable collaborating with SRE and scientific profiles on clinical trial data.

• Rigor in handling sensitive patient data.

• Demonstrated ownership, autonomy, accountability, a structured and detail-oriented approach, with a focus on automation and maintainability.

• A curious mindset with a commitment to continuous learning.

• Approximately 4–6 years of experience in data engineering, platform engineering, or backend roles with a substantial infrastructure component.

• Proven experience in operating production pipelines with accountability.

• Experience in writing and reviewing infrastructure as code within a team environment.

• Experience delivering data to stakeholders beyond one's immediate team.

• Familiarity with maintaining a codebase using Git workflows, pull requests, and CI practices.

• Experience working in contexts with stringent standards for security, compliance, or sensitive data.

• Experience in healthcare data, GDPR-heavy, or HDS-hosted environments is an advantage.

• Experience supporting scientific or clinical research users is a plus.

• Experience managing contractual data exchanges with external partners is a plus.

• Required English proficiency level selection from A1 to C2 in the application form.

🏝️ Benefits

• Flexible remote working environment.

• Practical application of AI technologies.

• A culture that emphasizes automation, observability, and continuous improvement.

• Direct engagement with clinical research teams and operational stakeholders.

• Complete ownership of the data platform and its infrastructure.

• A supportive environment dedicated to continuous learning.

• No specific monetary benefits, insurance, retirement plans, paid leave, equity, or bonuses mentioned.