Data Reliability Engineer

🏨 The company 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. 📝 Your 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. Your impact : 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. Your day-to-day : 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. ✨ Your team Your future teammates : Mélody Ballouard and Alric Gaurier — Data Reliability Engineers. The team owns the data pipeline end to end: ingestion, transformation, exports, and the infrastructure underneath. You'll be exposed to all of it from day one. Your manager : Alric Gaurier Team's extra : A small team with direct exposure to clinical research, medical and operational stakeholders. What you build is used by named people you talk to every week — not by an anonymous backlog. 👤 What we are looking for ⚙️ You are the right person if you can: 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 stream