Analytics Software 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.

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.

Your role

In a nutshell: You join the Data & Analytics team to build and scale key technical foundations (backend + modern data stack) so data is reliable, usable and accessible for clinical, research and business teams.

Your impact: Increase delivery speed and trust in insights by improving automation, quality and documentation—and by ensuring metrics are consistently defined and reused.

Your day-to-day:

• Build backend features for our Research Data Platform (Python/FastAPI, PostgreSQL).
• Build & maintain dbt models (business logic, tests, docs, repo standards) to keep the data model robust for Self-Service analytics.
• Work closely with internal users (Research/Clinical/Business) to iterate fast and deliver value.
• Participate in code reviews, technical design, and continuous improvement of engineering standards.
• Support the team to deliver BI solutions such as dashboards and/or business analysis
• Contribute to metrics governance via a semantic layer (e.g., Cube.js) to align KPIs.
• Turn recurring manual work (reporting, quality triage, source onboarding) into reliable tooling, leveraging AI responsibly.

Your team

• Your future teammates: Anna - Analytics Engineer
• Your manager: Jean-Baptiste - Analytics Lead

“We'll work together closely: my role is to find technical solutions with the Product and Platform engineering teams to answer data-related business problems, and I stay a hands-on contributor — you won't be alone in the code. I value relationships built on honesty and trust, and I really enjoy working with curious people who are always eager to learn and to help. I'm based in Biarritz and have a lot of fun doing triathlon”

• Team’s extra: A small team of three, with direct exposure to product, research and clinical questions. Regular team time in Biarritz — seminars, team meetings, surfing, hiking and every possible Basque country cliché.

What we are looking for

• You are the right person if you can:
  • Ship production-grade Python (tests, code quality, reviews, CI).
  • Build and maintain backend APIs (FastAPI/Flask/Django or equivalent).
  • Data modeling with advanced SQL to deliver production dbt models.
  • Use AI tools as a multiplier while keeping strong engineering ownership.
  • Deploy and operate what you build (containers, environments, CI/CD).
  • Implement data tests/alerting and troubleshoot data quality issues.
• You are the right person if you have:
  • A structured, detail-oriented approach and a bias for automation.
  • Strong collaboration skills across technical and non-technical partners.
  • Pragmatism: prioritizing impact and maintainability.
  • Ownership, autonomy and accountability.
  • Curiosity and a continuous-learning mindset.
  • Clear communication and a solution-oriented attitude.
• You are the right person if you have already:
  • ~3–5 years across data and software (typical path: analytics engineer moving toward software).
  • Worked in a maintained codebase (Git workflow, PRs, CI).
  • Shipped production dbt models with documentation and tests.
  • Contributed to self-service data tooling and enablement.
  • Collaborated with product/engineering teams on data topics.
  • Operated in a context with high standards (security, compliance, sensitive data).
• It’s a plus if you:
  • Have experience with semantic layers / metrics governance (Cube.js, LookML, dbt Semantic Layer…).
  • Have exposure to healthcare data or other GDPR-heavy environments.
  • Have supported scientific users (Jupyter, RStudio, study data).

Why join us

• A mission that holds up: improving patient care with data.
• Rare scope at the intersection of backend and modern data engineering.
• A culture focused on automation and continuous improvement, with practical AI usage.
• Remote-friendly setup and an environment that values quality, standards and ownership.

Hiring process

• Screening - Talent Acquisition Specialist (15 min): validate motivation and role prerequisites
• Manager Fit - Jean-Baptiste Pajot (45 min) : position fit, and an honest conversation about the engineering-first, hybrid nature of the role
• Technical Case Study - prep (max 2h) + live discussion (1h30): you'll rev