[Hiring] Data Reliability Engineer @Resilience Care

Role Description

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.

Qualifications

• Strong communication skills — this is not a nice-to-have in this role.
• The ability to translate an operational or scientific need into a technical specification.
• 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.
• Curiosity and a continuous-learning mindset.

Requirements

• ~4–6 years in data engineering, platform engineering, or backend with a strong infrastructure component.
• Operated production pipelines you were accountable for.
• Written and reviewed infrastructure as code in a team setting.
• Delivered data to stakeholders outside your own team, and handled the conversation that goes with it.
• Worked in a maintained codebase (Git workflow, PRs, CI).
• Operated in a context with high standards (security, compliance, sensitive data).

Benefits

• A mission that holds up: improving patient care with data.
• Real ownership over a full platform — pipelines and the infrastructure they run on.
• Direct contact with the people your work serves, including clinical research teams running active studies.
• A culture focused on automation, observability and continuous improvement, with practical AI usage.
• Remote-friendly setup and an environment that values quality, standards and ownership.

Hiring Process

• Screening - Elena (15 min): validate motivation and role prerequisites.
• Manager Fit - Alric (30 min): position fit, and an honest conversation about the ops-and-build nature of the role.
• Technical Case Study - live session (1h30): you work through a small realistic case with us — pipeline design, infrastructure and data export constraints.
• Team fit: Fit interview Mellody, Anna, Guillaume (30 min): collaboration fit with Research/Clinical and operational requesters.
• Culture fit - Elena (40 min): assess alignment with our culture and remote ways of working.
• Strategic Fit Jullian (30 min): collaboration fit, engineering standards.