Data Reliability Engineer

📋 Job Overview

You join the Data Reliability Engineering (DRE) team to build, operate and secure the data platform that carries healthcare data from our production services to the people who use it — internal operational teams, clinical research users, and external partners receiving contractual data exports. Data arrives on time, complete, traceable and secure — and when it doesn't, we know before the users do. You turn one-off data requests and manual export procedures into automated, monitored, reproducible pipelines.

🏢 About Resilience Care

Resilience Care is a leading medical remote monitoring player and a clinical research partner. Founded in France in 2021, our mission is simple: improve patient care. We build remote monitoring solutions in oncology, gastroenterology and psychiatry, powered by ePRO collection and AI techniques. Our platform helps care teams detect side effects early for continuous and proactive care, while our patient app helps people track and manage symptoms with tailored resources. Our solutions optimize care pathways, enrich continuous patient understanding, and accelerate clinical research through the collection, structuring and detailed analysis of real-world data. Today, our solutions are deployed in routine care for 35,000 patients across 200+ healthcare institutions, and also support around twenty academic and industry clinical studies. We put data at the service of care and therapeutic innovation, with the ambition to enable every patient to benefit from personalized medicine.

🎯 The Role

In a nutshell: You join the Data Reliability Engineering (DRE) team to build, operate and secure the data platform that carries healthcare data from our production services to the people who use it — internal operational teams, clinical research users, and external partners receiving contractual data exports.

✅ Key Responsibilities

• Build and operate data pipelines: ingestion from application events & databases, transformation, export.
• Build the tooling that handles those pipelines, hand in hand with SRE: infrastructure as code (Terraform, Helm on Kubernetes), deployment, CI/CD, secrets management, database operations and incident response.
• Watch over data quality and pipeline health: Grafana dashboards, alerting, and metric- and log-based observability, so failures are detected and diagnosed fast.
• Own data contracts and data quality: agree with the emitting squads on schemas, semantics and breaking-change rules, and enforce them with automated tests (freshness, completeness, referential integrity) that fail loudly before the data reaches its users.
• Deliver secure data exports to external partners and institutions.
• Provision and maintain dedicated PostgreSQL Research Spaces for clinical study teams, including access control and data scoping per study.
• Help the modelers scale their work: industrialize the dbt project so a single modelisation written by a Data Analyst is centralized, parallelized and applied across several targets.
• Work directly with the requesters: scope the need, challenge it, agree on what is feasible, deliver, and document.
• Work with the product squads on the data they emit: challenge their design upstream, and take part in the implementation on their side (TypeScript) when needed — you contribute to their code, they keep their roadmap.
• Participate in code reviews, technical design and continuous improvement of engineering standards.

📌 Required Qualifications

• Ship production-grade Python (tests, typing, code quality, reviews, CI), and use AI tools as a multiplier without letting go of engineering ownership.
• Build and operate data pipelines and orchestration (Prefect, Airflow, Dagster or equivalent), including event streams (Kafka) and object/file transports (S3, SFTP).
• Write and optimize advanced SQL on PostgreSQL (indexing, query plans, incremental loads).
• Push dbt well beyond basic usage: macros and Jinja, multi-target projects, incremental strategies, tests, packages and CI integration.
• Deploy and operate what you build (Terraform, Helm/Kubernetes, containers, CI/CD), with meaningful monitoring and alerting (Grafana or equivalent) — and lead the diagnosis when something breaks.
• Read and contribute to TypeScript codebases when the pipeline meets the application side, with a solid grasp of back-end API architecture (REST design, versioning, authentication, pagination, contracts).

⭐ Desirable Experience

• Strong communication skills — this is not a nice-to-have in this role. You will spend real time with non-technical requesters.
• The ability to translate an operational or scientific need into a technical specification, and to say no (and explain why) when a request is not feasible or not compliant — with the support of the Legal/Compliance department.
• Comfort collaborating with SRE on shared infrastructure, and with scientific profiles on clinical trial data.
• Rigor with sensitive data: you treat patient data with the caution it deserves, by reflex.
• Ownership, autonomy and accountability, including on-call-style responsibility for what you ship — with a structured, detail-oriented approach, a bias for automation, and a focus on impact and maintainability.
• Curiosity and a continuous-learning mindset — the reflex to go beyond your own stack: reading a product squad's code, digging into an API or an infrastructure layer you don't own, and learning what you need to unblock yourself instead of waiting for someone else.
• ~4–6 years in data engineering, platform engineering, or backend with a strong infrastructure component.
• Operated production pipelines

🎁 Benefits

Competitive

🛂 Visa & Eligibility

Not specified